Journal Description
Brain Sciences
Brain Sciences
is an international, peer-reviewed, open access journal on neuroscience published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, PMC, Embase, PSYNDEX, CAPlus / SciFinder, and other databases.
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.6 days after submission; acceptance to publication is undertaken in 2.5 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
3.3 (2022);
5-Year Impact Factor:
3.4 (2022)
Latest Articles
mTBI Biological Biomarkers as Predictors of Postconcussion Syndrome—Review
Brain Sci. 2024, 14(5), 513; https://doi.org/10.3390/brainsci14050513 (registering DOI) - 18 May 2024
Abstract
Postconcussion syndrome (PCS) is one of the leading complications that may appear in patients after mild head trauma. Every day, thousands of people, regardless of age, gender, and race, are diagnosed in emergency departments due to head injuries. Traumatic Brain Injury (TBI) is
[...] Read more.
Postconcussion syndrome (PCS) is one of the leading complications that may appear in patients after mild head trauma. Every day, thousands of people, regardless of age, gender, and race, are diagnosed in emergency departments due to head injuries. Traumatic Brain Injury (TBI) is a significant public health problem, impacting an estimated 1.5 million people in the United States and up to 69 million people worldwide each year, with 80% of these cases being mild. An analysis of the available research and a systematic review were conducted to search for a solution to predicting the occurrence of postconcussion syndrome. Particular biomarkers that can be examined upon admission to the emergency department after head injury were found as possible predictive factors of PCS development. Setting one unequivocal definition of PCS is still a challenge that causes inconsistent results. Neuron Specific Enolase (NSE), Glial Fibrillary Acidic Protein (GFAP), Ubiquitin C-terminal Hydrolase-L1 (UCH-L1), Serum Protein 100 B (s100B), and tau protein are found to be the best predictors of PCS development. The presence of all mentioned biomarkers is confirmed in severe TBI. All mentioned biomarkers are used as predictors of PCS. A combined examination of NSE, GFAP, UCH-1, S100B, and tau protein should be performed to detect mTBI and predict the development of PCS.
Full article
(This article belongs to the Section Molecular and Cellular Neuroscience)
Open AccessArticle
Parafoveal Processing of Orthography, Phonology, and Semantics during Chinese Reading: Effects of Foveal Load
by
Lei Zhang, Liangyue Kang, Wanying Chen, Fang Xie and Kayleigh L. Warrington
Brain Sci. 2024, 14(5), 512; https://doi.org/10.3390/brainsci14050512 (registering DOI) - 18 May 2024
Abstract
The foveal load hypothesis assumes that the ease (or difficulty) of processing the currently fixated word in a sentence can influence processing of the upcoming word(s), such that parafoveal preview is reduced when foveal load is high. Recent investigations using pseudo-character previews reported
[...] Read more.
The foveal load hypothesis assumes that the ease (or difficulty) of processing the currently fixated word in a sentence can influence processing of the upcoming word(s), such that parafoveal preview is reduced when foveal load is high. Recent investigations using pseudo-character previews reported an absence of foveal load effects in Chinese reading. Substantial Chinese studies to date provide some evidence to show that parafoveal words may be processed orthographically, phonologically, or semantically. However, it has not yet been established whether parafoveal processing is equivalent in terms of the type of parafoveal information extracted (orthographic, phonological, semantic) under different foveal load conditions. Accordingly, the present study investigated this issue with two experiments. Participants’ eye movements were recorded as they read sentences in which foveal load was manipulated by placing a low- or high-frequency word N preceding a critical word. The preview validity of the upcoming word N + 1 was manipulated in Experiment 1, and word N + 2 in Experiment 2. The parafoveal preview was either identical to word N + 1(or word N + 2); orthographically related; phonologically related; semantically related; or an unrelated pseudo-character. The results showed robust main effects of frequency and preview type on both N + 1 and N + 2. Crucially, however, interactions between foveal load and preview type were absent, indicating that foveal load does not modulate the types of parafoveal information processed during Chinese reading.
Full article
(This article belongs to the Section Neurolinguistics)
►▼
Show Figures
Figure 1
Figure 1
<p>An example of sentence materials in Experiment 1. Note. HF = high frequency; LF = low frequency; ID = identical preview; OR = orthographically related preview PH = phonologically related preview, SE = semantically related preview, PSE = pseudo-character preview, unrelated to target word. The pretarget word (N) is presented in italics, while the previews of the target (N + 1) is in bold (for illustration purposes only). The vertical black line represents the position of the invisible boundary. As readers’ eyes crossed the boundary, the preview was replaced by the target. High and low refer to the foveal load condition. The sentence for HF condition means ‘Young people in the new era believe that love can overcome all difficulties’. The sentence for LF condition means ‘Young people in the new era claim that love can overcome all difficulties’.</p> Full article ">Figure 2
<p>Figure shows the differences between preview types in SKIP. Note. Skipping is the best measure to show the differences between preview types.</p> Full article ">Figure 3
<p>An example of sentence materials in Experiment 2. Note. HF = high frequency; LF = low frequency; ID = identical preview; OR = orthographically related preview PH = phonologically related preview, SE = semantically related preview, PSE = pseudo-character preview, unrelated to target word. The pretarget word (N) is presented in italics, while the previews of the target (N + 2) is in bold (for illustration purposes only). The vertical black line represents the position of the invisible boundary. As readers’ eyes crossed the boundary, the preview was replaced by the target. High and low refer to the foveal load condition. The sentence for HF condition means ‘The Yao ethnic heritage inheritor is explaining how the colors for dyeing cloth are extracted’. The sentence for LF condition means ‘The Yao ethnic heritage inheritor is filming how the colors for dyeing cloth are extracted’.</p> Full article ">
<p>An example of sentence materials in Experiment 1. Note. HF = high frequency; LF = low frequency; ID = identical preview; OR = orthographically related preview PH = phonologically related preview, SE = semantically related preview, PSE = pseudo-character preview, unrelated to target word. The pretarget word (N) is presented in italics, while the previews of the target (N + 1) is in bold (for illustration purposes only). The vertical black line represents the position of the invisible boundary. As readers’ eyes crossed the boundary, the preview was replaced by the target. High and low refer to the foveal load condition. The sentence for HF condition means ‘Young people in the new era believe that love can overcome all difficulties’. The sentence for LF condition means ‘Young people in the new era claim that love can overcome all difficulties’.</p> Full article ">Figure 2
<p>Figure shows the differences between preview types in SKIP. Note. Skipping is the best measure to show the differences between preview types.</p> Full article ">Figure 3
<p>An example of sentence materials in Experiment 2. Note. HF = high frequency; LF = low frequency; ID = identical preview; OR = orthographically related preview PH = phonologically related preview, SE = semantically related preview, PSE = pseudo-character preview, unrelated to target word. The pretarget word (N) is presented in italics, while the previews of the target (N + 2) is in bold (for illustration purposes only). The vertical black line represents the position of the invisible boundary. As readers’ eyes crossed the boundary, the preview was replaced by the target. High and low refer to the foveal load condition. The sentence for HF condition means ‘The Yao ethnic heritage inheritor is explaining how the colors for dyeing cloth are extracted’. The sentence for LF condition means ‘The Yao ethnic heritage inheritor is filming how the colors for dyeing cloth are extracted’.</p> Full article ">
Open AccessReview
Mapping the Neural Basis of Neuroeconomics with Functional Magnetic Resonance Imaging: A Narrative Literature Review
by
Carlo A. Mallio, Andrea Buoso, Massimo Stiffi, Laura Cea, Daniele Vertulli, Caterina Bernetti, Gianfranco Di Gennaro, Martijn P. van den Heuvel and Bruno Beomonte Zobel
Brain Sci. 2024, 14(5), 511; https://doi.org/10.3390/brainsci14050511 (registering DOI) - 18 May 2024
Abstract
Neuroeconomics merges neuroscience, economics, and psychology to investigate the neural basis of decision making. Decision making involves assessing outcomes with subjective value, shaped by emotions and experiences, which are crucial in economic decisions. Functional MRI (fMRI) reveals key areas of the brain, including
[...] Read more.
Neuroeconomics merges neuroscience, economics, and psychology to investigate the neural basis of decision making. Decision making involves assessing outcomes with subjective value, shaped by emotions and experiences, which are crucial in economic decisions. Functional MRI (fMRI) reveals key areas of the brain, including the ventro-medial prefrontal cortex, that are involved in subjective value representation. Collaborative interdisciplinary efforts are essential for advancing the field of neuroeconomics, with implications for clinical interventions and policy design. This review explores subjective value in neuroeconomics, highlighting brain regions identified through fMRI studies.
Full article
(This article belongs to the Section Neurotechnology and Neuroimaging)
Open AccessSystematic Review
Virtual Reality Exposure Therapy for Treating Fear of Contamination Disorders: A Systematic Review of Healthy and Clinical Populations
by
Francesca Ferraioli, Laura Culicetto, Luca Cecchetti, Alessandra Falzone, Francesco Tomaiuolo, Angelo Quartarone and Carmelo Mario Vicario
Brain Sci. 2024, 14(5), 510; https://doi.org/10.3390/brainsci14050510 - 17 May 2024
Abstract
Virtual Reality Exposure Therapy (VRET), particularly immersive Virtual Reality Exposure Therapy (iVRET), has gained attraction as an innovative approach in exposure therapy (ET), notably for some anxiety disorders with a fear of contamination component, such as spider phobia (SP) and obsessive–compulsive disorder (OCD).
[...] Read more.
Virtual Reality Exposure Therapy (VRET), particularly immersive Virtual Reality Exposure Therapy (iVRET), has gained attraction as an innovative approach in exposure therapy (ET), notably for some anxiety disorders with a fear of contamination component, such as spider phobia (SP) and obsessive–compulsive disorder (OCD). This systematic work investigates iVRET’s effectiveness in modulating disgust emotion—a shared aberrant feature across these disorders. Recent reviews have evaluated VRET’s efficacy against in vivo ET. However, emerging evidence also highlights iVRET’s potential in diminishing atypical disgust and related avoidance behaviors, expanding beyond traditional fear-focused outcomes. Our systematic synthesis, adhering to PRISMA guidelines, aims to fill this gap by assessing iVRET’s efficacy in regulating disgust emotion within both clinical and at-risk populations, identified through standardized questionnaires and subjective disgust ratings. This research analyzes data from eight studies on clinical populations and five on healthy populations, offering an insight into iVRET’s potential to mitigate the aberrant disgust response, a common transdiagnostic feature in varied psychopathologies. The findings support iVRET’s clinical relevance in disgust management, providing evidence for a broader therapeutic application of iVRET and pointing out the need for more focused and complete investigations in this emergent field.
Full article
(This article belongs to the Section Behavioral Neuroscience)
Open AccessArticle
From Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder
by
Chunyu Pan, Ying Ma, Lifei Wang, Yan Zhang, Fei Wang and Xizhe Zhang
Brain Sci. 2024, 14(5), 509; https://doi.org/10.3390/brainsci14050509 - 17 May 2024
Abstract
Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain’s dynamic and complex nature, exploring its mechanisms from a network control standpoint
[...] Read more.
Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain’s dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain’s ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.
Full article
(This article belongs to the Special Issue Brain Network Connectivity Analysis in Neuroscience)
Open AccessReview
EEG Changes in Migraine—Can EEG Help to Monitor Attack Susceptibility?
by
Thomas C. van den Hoek, Mark van de Ruit, Gisela M. Terwindt and Else A. Tolner
Brain Sci. 2024, 14(5), 508; https://doi.org/10.3390/brainsci14050508 - 17 May 2024
Abstract
Migraine is a highly prevalent brain condition with paroxysmal changes in brain excitability believed to contribute to the initiation of an attack. The attacks and their unpredictability have a major impact on the lives of patients. Clinical management is hampered by a lack
[...] Read more.
Migraine is a highly prevalent brain condition with paroxysmal changes in brain excitability believed to contribute to the initiation of an attack. The attacks and their unpredictability have a major impact on the lives of patients. Clinical management is hampered by a lack of reliable predictors for upcoming attacks, which may help in understanding pathophysiological mechanisms to identify new treatment targets that may be positioned between the acute and preventive possibilities that are currently available. So far, a large range of studies using conventional hospital-based EEG recordings have provided contradictory results, with indications of both cortical hyper- as well as hypo-excitability. These heterogeneous findings may largely be because most studies were cross-sectional in design, providing only a snapshot in time of a patient’s brain state without capturing day-to-day fluctuations. The scope of this narrative review is to (i) reflect on current knowledge on EEG changes in the context of migraine, the attack cycle, and underlying pathophysiology; (ii) consider the effects of migraine treatment on EEG features; (iii) outline challenges and opportunities in using EEG for monitoring attack susceptibility; and (iv) discuss future applications of EEG in home-based settings.
Full article
(This article belongs to the Section Neuroscience of Pain)
Open AccessArticle
Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study
by
Shiting Qian, Qinqin Yang, Congbo Cai, Jiyang Dong and Shuhui Cai
Brain Sci. 2024, 14(5), 507; https://doi.org/10.3390/brainsci14050507 - 17 May 2024
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain’s intrinsic connectivity and capture dynamic changes in the brain. In
[...] Read more.
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain’s intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.
Full article
(This article belongs to the Special Issue New Perspectives in Treatment of Psychiatric Disorders: Focus on Neuroimaging)
►▼
Show Figures
Figure 1
Figure 1
<p>(<b>A</b>) Free energy estimation plot (red dot indicates minimum free energy point). (<b>B</b>) DFC matrix center-of-mass plots for the three best states are clustered by the HMM. Each state’s average probability of occurrence is given on top of each plot. The horizontal and vertical coordinates represent the ROI numbers based on the Dosenbach160 mapping. The red-dashed box highlights the significant enhancement of DFC in State 2.</p> Full article ">Figure 2
<p>The between-group difference in time-varying characteristics of (<b>A</b>) ALF of State 1, (<b>B</b>) FO of State 1, and (<b>C</b>) FO of State 2. The black dot’s value represents the box plot’s average value. * denotes significance level <span class="html-italic">p</span> < 0.05, *** denotes <span class="html-italic">p</span> < 0.001. ALF, averaged lifetime; ASD, autism spectrum disorder; FO, fractional occupancy; HC, healthy control.</p> Full article ">Figure 3
<p>Correlation analysis between ADOS and ALF. (<b>A</b>) ADOS total vs. ALF. (<b>B</b>) ADOS Gotham vs. ALF. (<b>C</b>) ADOS social vs. ALF. (<b>D</b>) ADOS Gotham RRB vs. ALF. The black dots represent the value of HMM time-varying characteristic indicators corresponding to different clinical scale scoring values. The green area in each figure represents confidence intervals. ADOS, Autism Diagnostic Observation Schedule; RRB, restricted and repetitive behaviors.</p> Full article ">Figure 4
<p>Correlation analysis between ADOS and FO. (<b>A</b>) ADOS total vs. FO. (<b>B</b>) ADOS comm vs. FO. (<b>C</b>) ADOS social vs. FO. (<b>D</b>) ADOS Gotham RRB vs. FO. The black dots represent the value of HMM time-varying characteristic indicators corresponding to different clinical scale scoring values. The green area in each figure represents confidence intervals. FO, fractional occupancy.</p> Full article ">Figure 5
<p>Correlation analysis between ADI-R and HMM dynamic temporal attributes. (<b>A</b>) ADI-R social total A vs. ALF. (<b>B</b>) ADI-R social total A vs. FO. (<b>C</b>) ADI-R social total A vs. SR. The black dots represent the value of HMM time-varying characteristic indicators corresponding to different clinical scale scoring values. The green area in each figure represents confidence intervals. ADI-R, autism diagnostic interview-revised; SR, switching rate.</p> Full article ">Figure 6
<p>The visualization of between-group differences in (<b>A</b>) Betweenness Centrality, (<b>B</b>) Degree Centrality, and (<b>C</b>) Nodal Clustering Coefficient. Blue color indicates ASD < HC and orange color indicates ASD > HC. ACC, anterior cingulate cortex; dACC, dorsal anterior cingulate cortex; IPL, inferior parietal lobe; SMA, supplementary motor area; vFC, ventromedial frontal cortex; vmPFC, ventromedial prefrontal cortex.</p> Full article ">
<p>(<b>A</b>) Free energy estimation plot (red dot indicates minimum free energy point). (<b>B</b>) DFC matrix center-of-mass plots for the three best states are clustered by the HMM. Each state’s average probability of occurrence is given on top of each plot. The horizontal and vertical coordinates represent the ROI numbers based on the Dosenbach160 mapping. The red-dashed box highlights the significant enhancement of DFC in State 2.</p> Full article ">Figure 2
<p>The between-group difference in time-varying characteristics of (<b>A</b>) ALF of State 1, (<b>B</b>) FO of State 1, and (<b>C</b>) FO of State 2. The black dot’s value represents the box plot’s average value. * denotes significance level <span class="html-italic">p</span> < 0.05, *** denotes <span class="html-italic">p</span> < 0.001. ALF, averaged lifetime; ASD, autism spectrum disorder; FO, fractional occupancy; HC, healthy control.</p> Full article ">Figure 3
<p>Correlation analysis between ADOS and ALF. (<b>A</b>) ADOS total vs. ALF. (<b>B</b>) ADOS Gotham vs. ALF. (<b>C</b>) ADOS social vs. ALF. (<b>D</b>) ADOS Gotham RRB vs. ALF. The black dots represent the value of HMM time-varying characteristic indicators corresponding to different clinical scale scoring values. The green area in each figure represents confidence intervals. ADOS, Autism Diagnostic Observation Schedule; RRB, restricted and repetitive behaviors.</p> Full article ">Figure 4
<p>Correlation analysis between ADOS and FO. (<b>A</b>) ADOS total vs. FO. (<b>B</b>) ADOS comm vs. FO. (<b>C</b>) ADOS social vs. FO. (<b>D</b>) ADOS Gotham RRB vs. FO. The black dots represent the value of HMM time-varying characteristic indicators corresponding to different clinical scale scoring values. The green area in each figure represents confidence intervals. FO, fractional occupancy.</p> Full article ">Figure 5
<p>Correlation analysis between ADI-R and HMM dynamic temporal attributes. (<b>A</b>) ADI-R social total A vs. ALF. (<b>B</b>) ADI-R social total A vs. FO. (<b>C</b>) ADI-R social total A vs. SR. The black dots represent the value of HMM time-varying characteristic indicators corresponding to different clinical scale scoring values. The green area in each figure represents confidence intervals. ADI-R, autism diagnostic interview-revised; SR, switching rate.</p> Full article ">Figure 6
<p>The visualization of between-group differences in (<b>A</b>) Betweenness Centrality, (<b>B</b>) Degree Centrality, and (<b>C</b>) Nodal Clustering Coefficient. Blue color indicates ASD < HC and orange color indicates ASD > HC. ACC, anterior cingulate cortex; dACC, dorsal anterior cingulate cortex; IPL, inferior parietal lobe; SMA, supplementary motor area; vFC, ventromedial frontal cortex; vmPFC, ventromedial prefrontal cortex.</p> Full article ">
Open AccessArticle
The Effect of Rhythmic Audio-Visual Stimulation on Inhibitory Control: An ERP Study
by
Yifan Wang, Di Wu, Kewei Sun, Yan Zhu, Xianglong Chen and Wei Xiao
Brain Sci. 2024, 14(5), 506; https://doi.org/10.3390/brainsci14050506 - 17 May 2024
Abstract
Inhibitory control, as an essential cognitive ability, affects the development of higher cognitive functions. Rhythmic perceptual stimulation has been used to improve cognitive abilities. It is unclear, however, whether it can be used to improve inhibitory control. This study used the Go/NoGo task
[...] Read more.
Inhibitory control, as an essential cognitive ability, affects the development of higher cognitive functions. Rhythmic perceptual stimulation has been used to improve cognitive abilities. It is unclear, however, whether it can be used to improve inhibitory control. This study used the Go/NoGo task and the Stroop task to assess various levels of inhibitory control using rhythmic audio-visual stimuli as the stimulus mode. Sixty subjects were randomly divided into three groups to receive 6 Hz, 10 Hz, and white noise stimulation for 30 min. Two tasks were completed by each subject both before and after the stimulus. Before and after the task, closed-eye resting EEG data were collected. The results showed no differences in behavioral and EEG measures of the Go/NoGo task among the three groups. While both 6 Hz and 10 Hz audio-visual stimulation reduced the conflict effect in the Stroop task, only 6 Hz audio-visual stimulation improved the amplitude of the N2 component and decreased the conflict score. Although rhythmic audio-visual stimulation did not enhance response inhibition, it improved conflict inhibition.
Full article
(This article belongs to the Section Neuropsychology)
►▼
Show Figures
Figure 1
Figure 1
<p>Experimental design. Rhythmic audio-visual stimulation lasted 30 min. Stroop task and Go/NoGo task were executed before and after experiment, and EEG data were collected throughout experiment.</p> Full article ">Figure 2
<p>Task procedure. (<b>A</b>) Go/NoGo task; (<b>B</b>) Stroop task.</p> Full article ">Figure 3
<p>In the Stroop task, means and standard errors for Stroop effects and conflict scores were calculated. (<b>A</b>) Stroop effects in the 6 Hz, 10 Hz, and white noise groups are shown. (<b>B</b>) Conflict scores in the 6 Hz, 10 Hz, and white noise groups are shown. The error bars represent the standard error; <span class="html-italic">p</span> < 0.001 ***.</p> Full article ">Figure 4
<p>In the Go/NoGo task, the response time of the go trials and the correct rejection rate for the no-go trials were calculated. (<b>A</b>) The go-trial response times in the 6 Hz, 10 Hz, and white noise groups are shown. (<b>B</b>) For the no-go trials, the correct rejection rate in the 6 Hz, 10 Hz, and white noise groups is shown. The error bars represent the standard error.</p> Full article ">Figure 5
<p>(<b>A</b>) Average ERP waveform in Stroop task. (<b>B</b>) Average ERP waveform in Go/NoGo task. Note: pre_6 Hz, pre_10 Hz, and pre_white represent baseline measurements of task before stimulation; post_6 Hz, post_10 Hz, and post_white represent task measurements after stimulation.</p> Full article ">Figure 5 Cont.
<p>(<b>A</b>) Average ERP waveform in Stroop task. (<b>B</b>) Average ERP waveform in Go/NoGo task. Note: pre_6 Hz, pre_10 Hz, and pre_white represent baseline measurements of task before stimulation; post_6 Hz, post_10 Hz, and post_white represent task measurements after stimulation.</p> Full article ">
<p>Experimental design. Rhythmic audio-visual stimulation lasted 30 min. Stroop task and Go/NoGo task were executed before and after experiment, and EEG data were collected throughout experiment.</p> Full article ">Figure 2
<p>Task procedure. (<b>A</b>) Go/NoGo task; (<b>B</b>) Stroop task.</p> Full article ">Figure 3
<p>In the Stroop task, means and standard errors for Stroop effects and conflict scores were calculated. (<b>A</b>) Stroop effects in the 6 Hz, 10 Hz, and white noise groups are shown. (<b>B</b>) Conflict scores in the 6 Hz, 10 Hz, and white noise groups are shown. The error bars represent the standard error; <span class="html-italic">p</span> < 0.001 ***.</p> Full article ">Figure 4
<p>In the Go/NoGo task, the response time of the go trials and the correct rejection rate for the no-go trials were calculated. (<b>A</b>) The go-trial response times in the 6 Hz, 10 Hz, and white noise groups are shown. (<b>B</b>) For the no-go trials, the correct rejection rate in the 6 Hz, 10 Hz, and white noise groups is shown. The error bars represent the standard error.</p> Full article ">Figure 5
<p>(<b>A</b>) Average ERP waveform in Stroop task. (<b>B</b>) Average ERP waveform in Go/NoGo task. Note: pre_6 Hz, pre_10 Hz, and pre_white represent baseline measurements of task before stimulation; post_6 Hz, post_10 Hz, and post_white represent task measurements after stimulation.</p> Full article ">Figure 5 Cont.
<p>(<b>A</b>) Average ERP waveform in Stroop task. (<b>B</b>) Average ERP waveform in Go/NoGo task. Note: pre_6 Hz, pre_10 Hz, and pre_white represent baseline measurements of task before stimulation; post_6 Hz, post_10 Hz, and post_white represent task measurements after stimulation.</p> Full article ">
Open AccessArticle
Associations between Total Atherosclerosis Burden of Baroreceptor-Resident Arteries and ECG Abnormalities after Acute Ischemic Stroke
by
Zhiyong Fu, Xin Ma, Xiaoxi Zhao, Xiangying Du and Yungao Wan
Brain Sci. 2024, 14(5), 505; https://doi.org/10.3390/brainsci14050505 - 16 May 2024
Abstract
Electrocardiogram (ECG) abnormalities are the most common cardiac complications after acute ischemic stroke (AIS) and predict poor outcomes. The arterial baroreflex is an essential determinant of cardiovascular autonomic regulation, with receptors mainly residing in carotid sinuses and aortic arch. The atherosclerosis of these
[...] Read more.
Electrocardiogram (ECG) abnormalities are the most common cardiac complications after acute ischemic stroke (AIS) and predict poor outcomes. The arterial baroreflex is an essential determinant of cardiovascular autonomic regulation, with receptors mainly residing in carotid sinuses and aortic arch. The atherosclerosis of these baroreceptor-resident arteries (BRA) is very common in AIS patients and might impair baroreflex function. However, the associations between the atherosclerosis of BRA and ECG abnormalities after AIS are still unknown. In total, 228 AIS patients within 7 days after onset without a pre-existing heart disease were prospectively recruited. With computed tomography angiography, atherosclerosis conditions in 10 segments of the carotid sinuses and aortic arch were scored and summed as the Total Atherosclerosis Burden of BRA (TAB-BRA), and asymptomatic coronary artery stenosis (ACAS) ≥50% was simultaneously assessed. We performed 12-lead ECG to dynamically detect abnormal repolarization, and 24 h Holter ECG to monitor arrhythmias and heart rate variability (HRV) parameters, which are reliable indicators to assess cardiac autonomic function. We found that TAB-BRA was positively associated with abnormal repolarization (OR 1.09; CI% 1.03–1.16; p = 0.003) and serious cardiac arrhythmias (OR 1.08; CI% 1.01–1.15; p = 0.021). In addition, TAB-BRA was an important predictor of abnormal repolarization, persisting over 3 days (OR 1.17; CI% 1.05–1.30; p = 0.003). However, ACAS ≥ 50% did not relate to these ECG abnormalities. TAB-BRA was negatively correlated with parasympathetic-related HRV parameters. Our results indicated that AIS patients with a high TAB-BRA are more likely to have ECG abnormalities and delayed normalization, which may relate to the decreased cardiac parasympathetic activity, but not the accompanied ACAS ≥ 50%.
Full article
(This article belongs to the Topic Diagnosis and Management of Acute Ischemic Stroke)
►▼
Show Figures
Figure 1
Figure 1
<p>Illustration of TAB-BRA assessment and its indicative value for abnormal repolarization persisting over 3 days after acute ischemic stroke. (<b>A</b>) Atherosclerosis conditions in 10 segments of BRA were, respectively, scored with 0 to 4 points according to the percentage of vessel circumference affected by atherosclerosis on orthogonal views (0, none; 1, <25%; 2, 25–49%; 3, 50–74%; 4, ≥75%), then summed as TAB-BRA. (<b>B</b>) An acute right insular infarction (white arrow) patient with a low TAB-BRA score (1 point) had a complicated abnormal repolarization (prolonged QTc), and it quickly normalized within 3 days. (<b>C</b>) An acute right insular infarction (white arrow) patient with high TAB-BRA scores (17 points) had a complicated abnormal repolarization (prolonged QTc), but it persisted over 3 days, and new atrial fibrillation was detected. (1) Craniocervical computed tomography angiography; (2) brain magnetic resonance imaging of diffusion-weighted image sequence; (3) initial ECG within 3 days of onset; (4) ECG after 3 days of onset. Abbreviations: TAB-BRA = Total Atherosclerosis Burden of Baroreceptor-Resident Arteries.</p> Full article ">Figure 2
<p>Occurrence of ECG abnormalities in acute ischemic stroke patients with ACAS ≥ 50% and without ACAS ≥ 50%. Abbreviations: ACAS = asymptomatic coronary artery stenosis; SCA = serious cardiac arrhythmias.</p> Full article ">Figure 3
<p>Scatter plots of TAB-BRA and heart rate variability parameters. (<b>A</b>,<b>B</b>): TAB-BRA vs two time-domain measures (rMSSD and SDNN, respectively), green color indicates 95% confidence interval band. (<b>C</b>–<b>F</b>): TAB-BRA vs four frequency-domain measures (HF, LF, TP and LF/HF, respectively), purple color indicates 95% confidence interval band. Abbreviations: TAB-BRA = Total Atherosclerosis Burden of Baroreceptor-Resident Arteries; rMSSD = root mean square of successive difference in RR interval; SDNN = SD of all normal-to-normal RR intervals; HF = high frequency; LF = low frequency; and TP = total power.</p> Full article ">
<p>Illustration of TAB-BRA assessment and its indicative value for abnormal repolarization persisting over 3 days after acute ischemic stroke. (<b>A</b>) Atherosclerosis conditions in 10 segments of BRA were, respectively, scored with 0 to 4 points according to the percentage of vessel circumference affected by atherosclerosis on orthogonal views (0, none; 1, <25%; 2, 25–49%; 3, 50–74%; 4, ≥75%), then summed as TAB-BRA. (<b>B</b>) An acute right insular infarction (white arrow) patient with a low TAB-BRA score (1 point) had a complicated abnormal repolarization (prolonged QTc), and it quickly normalized within 3 days. (<b>C</b>) An acute right insular infarction (white arrow) patient with high TAB-BRA scores (17 points) had a complicated abnormal repolarization (prolonged QTc), but it persisted over 3 days, and new atrial fibrillation was detected. (1) Craniocervical computed tomography angiography; (2) brain magnetic resonance imaging of diffusion-weighted image sequence; (3) initial ECG within 3 days of onset; (4) ECG after 3 days of onset. Abbreviations: TAB-BRA = Total Atherosclerosis Burden of Baroreceptor-Resident Arteries.</p> Full article ">Figure 2
<p>Occurrence of ECG abnormalities in acute ischemic stroke patients with ACAS ≥ 50% and without ACAS ≥ 50%. Abbreviations: ACAS = asymptomatic coronary artery stenosis; SCA = serious cardiac arrhythmias.</p> Full article ">Figure 3
<p>Scatter plots of TAB-BRA and heart rate variability parameters. (<b>A</b>,<b>B</b>): TAB-BRA vs two time-domain measures (rMSSD and SDNN, respectively), green color indicates 95% confidence interval band. (<b>C</b>–<b>F</b>): TAB-BRA vs four frequency-domain measures (HF, LF, TP and LF/HF, respectively), purple color indicates 95% confidence interval band. Abbreviations: TAB-BRA = Total Atherosclerosis Burden of Baroreceptor-Resident Arteries; rMSSD = root mean square of successive difference in RR interval; SDNN = SD of all normal-to-normal RR intervals; HF = high frequency; LF = low frequency; and TP = total power.</p> Full article ">
Open AccessSystematic Review
Neuronal Correlates of Empathy: A Systematic Review of Event-Related Potentials Studies in Perceptual Tasks
by
Rita Almeida, Catarina Prata, Mariana R. Pereira, Fernando Barbosa and Fernando Ferreira-Santos
Brain Sci. 2024, 14(5), 504; https://doi.org/10.3390/brainsci14050504 - 16 May 2024
Abstract
Empathy is a crucial component to infer and understand others’ emotions. However, a synthesis of studies regarding empathy and its neuronal correlates in perceptual tasks using event-related potentials (ERPs) has yet to occur. The current systematic review aimed to provide that overview. Upon
[...] Read more.
Empathy is a crucial component to infer and understand others’ emotions. However, a synthesis of studies regarding empathy and its neuronal correlates in perceptual tasks using event-related potentials (ERPs) has yet to occur. The current systematic review aimed to provide that overview. Upon bibliographic research, 30 studies featuring empathy assessments and at least one perceptual task measuring ERP components in healthy participants were included. Four main focus categories were identified, as follows: Affective Pictures, Facial Stimuli, Mental States, and Social Language. The Late Positive Potential was the most analyzed in Affective Pictures and was reported to be positively correlated with cognitive and affective empathy, along with other late components. In contrast, for Facial Stimuli, early components presented significant correlations with empathy scales. Particularly, the N170 presented negative correlations with cognitive and affective empathy. Finally, augmented N400 was suggested to be associated with higher empathy scores in the Mental States and Social Language categories. These findings highlight the relevance of early perceptual stages of empathic processing and how different EEG/ERP methodologies provide relevant information.
Full article
(This article belongs to the Special Issue EEG and Event-Related Potentials)
►▼
Show Figures
Figure 1
Open AccessArticle
Prefrontal Cortex Responses to Social Video Stimuli in Young Children with and without Autism Spectrum Disorder
by
Candida Barreto, Adrian Curtin, Yigit Topoglu, Jessica Day-Watkins, Brigid Garvin, Grant Foster, Zuhal Ormanoglu, Elisabeth Sheridan, James Connell, David Bennett, Karen Heffler and Hasan Ayaz
Brain Sci. 2024, 14(5), 503; https://doi.org/10.3390/brainsci14050503 - 16 May 2024
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting individuals worldwide and characterized by deficits in social interaction along with the presence of restricted interest and repetitive behaviors. Despite decades of behavioral research, little is known about the brain mechanisms that influence social
[...] Read more.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting individuals worldwide and characterized by deficits in social interaction along with the presence of restricted interest and repetitive behaviors. Despite decades of behavioral research, little is known about the brain mechanisms that influence social behaviors among children with ASD. This, in part, is due to limitations of traditional imaging techniques specifically targeting pediatric populations. As a portable and scalable optical brain monitoring technology, functional near infrared spectroscopy (fNIRS) provides a measure of cerebral hemodynamics related to sensory, motor, or cognitive function. Here, we utilized fNIRS to investigate the prefrontal cortex (PFC) activity of young children with ASD and with typical development while they watched social and nonsocial video clips. The PFC activity of ASD children was significantly higher for social stimuli at medial PFC, which is implicated in social cognition/processing. Moreover, this activity was also consistently correlated with clinical measures, and higher activation of the same brain area only during social video viewing was associated with more ASD symptoms. This is the first study to implement a neuroergonomics approach to investigate cognitive load in response to realistic, complex, and dynamic audiovisual social stimuli for young children with and without autism. Our results further confirm that new generation of portable fNIRS neuroimaging can be used for ecologically valid measurements of the brain function of toddlers and preschool children with ASD.
Full article
(This article belongs to the Special Issue Biomarkers Identification for Neurological Diseases and Neurorehabilitation)
►▼
Show Figures
Figure 1
Figure 1
<p>(<b>A</b>). fNIRS flat headband sensor and equivalent positions in the prefrontal cortex (left panel). Red and blue dots represent light emitters and detectors, respectively. Numbers (1–16) in between each pair represent the fNIRS optodes. (<b>B</b>). Data collection setup with a pilot participant in front of screen and camera (right panel).</p> Full article ">Figure 2
<p>Mean TELE-ASD-PEDS Total Likert Scores. ASD = Autism Spectrum Disorder group, TD = typically developing group. * t (14.81) = 8.84, <span class="html-italic">p</span> < 0.001.</p> Full article ">Figure 3
<p>Projection of optode 10 on the brain surface image (<b>left</b>) and mean HbO results (<b>right</b>) by <span class="html-italic">group</span> (ASD × TD) and <span class="html-italic">condition</span> (Social × Nonsocial) shows significant interaction (F<sub>1,69.6</sub> = 9.27, <span class="html-italic">p</span> = 0.003, η<sup>2</sup> = 0.12), and with post hoc comparisons, significantly higher ASD-Social compared to ASD-Nonsocial (**, F<sub>1,70.2</sub> = 19.61, <span class="html-italic">p</span> < 0.0001, η<sup>2</sup> = 0.22) and significantly higher ASD-Social compared to TD-Social (*, F<sub>1,39</sub> = 6.78, <span class="html-italic">p</span> < 0.03, η<sup>2</sup> = 0.15).</p> Full article ">
<p>(<b>A</b>). fNIRS flat headband sensor and equivalent positions in the prefrontal cortex (left panel). Red and blue dots represent light emitters and detectors, respectively. Numbers (1–16) in between each pair represent the fNIRS optodes. (<b>B</b>). Data collection setup with a pilot participant in front of screen and camera (right panel).</p> Full article ">Figure 2
<p>Mean TELE-ASD-PEDS Total Likert Scores. ASD = Autism Spectrum Disorder group, TD = typically developing group. * t (14.81) = 8.84, <span class="html-italic">p</span> < 0.001.</p> Full article ">Figure 3
<p>Projection of optode 10 on the brain surface image (<b>left</b>) and mean HbO results (<b>right</b>) by <span class="html-italic">group</span> (ASD × TD) and <span class="html-italic">condition</span> (Social × Nonsocial) shows significant interaction (F<sub>1,69.6</sub> = 9.27, <span class="html-italic">p</span> = 0.003, η<sup>2</sup> = 0.12), and with post hoc comparisons, significantly higher ASD-Social compared to ASD-Nonsocial (**, F<sub>1,70.2</sub> = 19.61, <span class="html-italic">p</span> < 0.0001, η<sup>2</sup> = 0.22) and significantly higher ASD-Social compared to TD-Social (*, F<sub>1,39</sub> = 6.78, <span class="html-italic">p</span> < 0.03, η<sup>2</sup> = 0.15).</p> Full article ">
Open AccessArticle
A Computational Model for the Simulation of Prepulse Inhibition and Its Modulation by Cortical and Subcortical Units
by
Thiago Ohno Bezerra, Antonio C. Roque and Cristiane Salum
Brain Sci. 2024, 14(5), 502; https://doi.org/10.3390/brainsci14050502 - 15 May 2024
Abstract
The sensorimotor gating is a nervous system function that modulates the acoustic startle response (ASR). Prepulse inhibition (PPI) phenomenon is an operational measure of sensorimotor gating, defined as the reduction of ASR when a high intensity sound (pulse) is preceded in milliseconds by
[...] Read more.
The sensorimotor gating is a nervous system function that modulates the acoustic startle response (ASR). Prepulse inhibition (PPI) phenomenon is an operational measure of sensorimotor gating, defined as the reduction of ASR when a high intensity sound (pulse) is preceded in milliseconds by a weaker stimulus (prepulse). Brainstem nuclei are associated with the mediation of ASR and PPI, whereas cortical and subcortical regions are associated with their modulation. However, it is still unclear how the modulatory units can influence PPI. In the present work, we developed a computational model of a neural circuit involved in the mediation (brainstem units) and modulation (cortical and subcortical units) of ASR and PPI. The activities of all units were modeled by the leaky-integrator formalism for neural population. The model reproduces basic features of PPI observed in experiments, such as the effects of changes in interstimulus interval, prepulse intensity, and habituation of ASR. The simulation of GABAergic and dopaminergic drugs impaired PPI by their effects over subcortical units activity. The results show that subcortical units constitute a central hub for PPI modulation. The presented computational model offers a valuable tool to investigate the neurobiology associated with disorder-related impairments in PPI.
Full article
(This article belongs to the Special Issue Understanding Mental Disorders via Computational Brain Network Modeling)
Open AccessArticle
Use of Ordered Beta Regression Unveils Cognitive Flexibility Index and Longitudinal Cognitive Training Signatures in Normal and Alzheimer’s Disease Pathological Aging
by
Daniel Alveal-Mellado and Lydia Giménez-Llort
Brain Sci. 2024, 14(5), 501; https://doi.org/10.3390/brainsci14050501 - 15 May 2024
Abstract
Generalized linear mixed models (GLMMs) are a cornerstone data analysis strategy in behavioral research because of their robustness in handling non-normally distributed variables. Recently, their integration with ordered beta regression (OBR), a novel statistical tool for managing percentage data, has opened new avenues
[...] Read more.
Generalized linear mixed models (GLMMs) are a cornerstone data analysis strategy in behavioral research because of their robustness in handling non-normally distributed variables. Recently, their integration with ordered beta regression (OBR), a novel statistical tool for managing percentage data, has opened new avenues for analyzing continuous response data. Here, we applied this combined approach to investigate nuanced differences between the 3xTg-AD model of Alzheimer’s disease (AD) and their C57BL/6 non-transgenic (NTg) counterparts with normal aging in a 5-day Morris Water Maze (MWM) test protocol. Our longitudinal study included 22 3xTg-AD mice and 15 NTg mice (both male and female) assessed at 12 and 16 months of age. By identifying and analyzing multiple swimming strategies during three different paradigms (cue, place task, and removal), we uncovered genotypic differences in all paradigms. Thus, the NTg group exhibited a higher percentage of direct search behaviors, while an association between circling episodes and 3xTg-AD animals was found. Furthermore, we also propose a novel metric—the “Cognitive Flexibility Index”—which proved sensitive in detecting sex-related differences. Overall, our integrated GLMMs-OBR approach provides a comprehensive insight into mouse behavior in the MWM test, shedding light on the effects of aging and AD pathology.
Full article
(This article belongs to the Special Issue Animal Models of Neurological Disorders)
Open AccessArticle
Beauty and Paintings: Aesthetic Experience in Patients with Behavioral Variant Frontotemporal Dementia When Viewing Abstract and Concrete Paintings
by
Claire Boutoleau-Bretonnière, Catherine Thomas-Anterion, Anne-Laure Deruet, Estelle Lamy and Mohamad El Haj
Brain Sci. 2024, 14(5), 500; https://doi.org/10.3390/brainsci14050500 - 15 May 2024
Abstract
We assessed the aesthetic experience of patients with behavioral variant frontotemporal dementia (bvFTD) to understand their ability to experience feelings of the sublime and to be moved when viewing paintings. We exposed patients with bvFTD and control participants to concrete and abstract paintings
[...] Read more.
We assessed the aesthetic experience of patients with behavioral variant frontotemporal dementia (bvFTD) to understand their ability to experience feelings of the sublime and to be moved when viewing paintings. We exposed patients with bvFTD and control participants to concrete and abstract paintings and asked them how moved they were by these paintings and whether the latter were beautiful or ugly. Patients with bvFTD declared being less moved than control participants by both abstract and concrete paintings. No significant differences were observed between abstract and concrete paintings in both patients with bvFTD and control participants. Patients with bvFTD provided fewer “beautiful” and more “ugly” responses than controls for both abstract and concrete paintings. No significant differences in terms of “beautiful” and “ugly” responses were observed between abstract and concrete paintings in both patients with bvFTD and control participants. These findings suggest disturbances in the basic affective experience of patients with bvFTD when they are exposed to paintings, as well as a bias in their ability to judge the aesthetic quality of paintings.
Full article
(This article belongs to the Section Neurodegenerative Diseases)
Open AccessArticle
No Consistent Antidepressant Effects of Deep Brain Stimulation of the Bed Nucleus of the Stria Terminalis
by
Paul B. Fitzgerald, Kate Hoy, Karyn E. Richardson, Kirsten Gainsford, Rebecca Segrave, Sally E. Herring, Zafiris J. Daskalakis and Richard G. Bittar
Brain Sci. 2024, 14(5), 499; https://doi.org/10.3390/brainsci14050499 - 15 May 2024
Abstract
Background: Applying deep brain stimulation (DBS) to several brain regions has been investigated in attempts to treat highly treatment-resistant depression, with variable results. Our initial pilot data suggested that the bed nucleus of the stria terminalis (BNST) could be a promising therapeutic target.
[...] Read more.
Background: Applying deep brain stimulation (DBS) to several brain regions has been investigated in attempts to treat highly treatment-resistant depression, with variable results. Our initial pilot data suggested that the bed nucleus of the stria terminalis (BNST) could be a promising therapeutic target. Objective: The aim of this study was to gather blinded data exploring the efficacy of applying DBS to the BNST in patients with highly refractory depression. Method: Eight patients with chronic severe treatment-resistant depression underwent DBS to the BNST. A randomised, double-blind crossover study design with fixed stimulation parameters was followed and followed by a period of open-label stimulation. Results: During the double-blind crossover phase, no consistent antidepressant effects were seen with any of the four stimulation parameters applied, and no patients achieved response or remission criteria during the blinded crossover phase or during a subsequent period of three months of blinded stimulation. Stimulation-related side effects, especially agitation, were reported by a number of patients and were reversible with adjustment of the stimulation parameters. Conclusions: The results of this study do not support the application of DBS to the BNST in patients with highly resistant depression or ongoing research utilising stimulation at this brain site. The blocked randomised study design utilising fixed stimulation parameters was poorly tolerated by the participants and does not appear suitable for assessing the efficacy of DBS at this location.
Full article
(This article belongs to the Special Issue Brain Stimulation for Psychiatric Disorders: Emerging Evidence and New Perspectives)
►▼
Show Figures
Figure 1
Open AccessArticle
Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain–Computer Interface
by
Dingyong Huang, Yingjie Wang, Liangwei Fan, Yang Yu, Ziyu Zhao, Pu Zeng, Kunqing Wang, Na Li and Hui Shen
Brain Sci. 2024, 14(5), 498; https://doi.org/10.3390/brainsci14050498 - 15 May 2024
Abstract
In this study, we investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct subject-driven cognitive states: resting state, narrative memory, music, and subtraction tasks. EEG data were collected from seven healthy male participants while performing these cognitive tasks, and
[...] Read more.
In this study, we investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct subject-driven cognitive states: resting state, narrative memory, music, and subtraction tasks. EEG data were collected from seven healthy male participants while performing these cognitive tasks, and the raw EEG signals were transformed into time–frequency maps using continuous wavelet transform. Based on these time–frequency maps, we developed a convolutional neural network model (TF-CNN-CFA) with a channel and frequency attention mechanism to automatically distinguish between these cognitive states. The experimental results demonstrated that the model achieved an average classification accuracy of 76.14% in identifying these four cognitive states, significantly outperforming traditional EEG signal processing methods and other classical image classification algorithms. Furthermore, we investigated the impact of varying lengths of EEG signals on classification performance and found that TF-CNN-CFA demonstrates consistent performance across different window lengths, indicating its strong generalization capability. This study validates the ability of EEG to differentiate higher cognitive states, which could potentially offer a novel BCI paradigm.
Full article
(This article belongs to the Section Social Cognitive and Affective Neuroscience)
Open AccessArticle
Unraveling the Influence of Litter Size, Maternal Care, Exercise, and Aging on Neurobehavioral Plasticity and Dentate Gyrus Microglia Dynamics in Male Rats
by
Lane Viana Krejcová, João Bento-Torres, Daniel Guerreiro Diniz, Antonio Pereira, Jr., Manuella Batista-de-Oliveira, Andreia Albuquerque Cunha Lopes de Morais, Rosângela Figueiredo Mendes-da-Silva, Ricardo Abadie-Guedes, Ângela Amâncio dos Santos, Denise Sandrelly Lima, Rubem Carlos Araujo Guedes and Cristovam Wanderley Picanço-Diniz
Brain Sci. 2024, 14(5), 497; https://doi.org/10.3390/brainsci14050497 - 15 May 2024
Abstract
This study explores the multifaceted influence of litter size, maternal care, exercise, and aging on rats’ neurobehavioral plasticity and dentate gyrus microglia dynamics. Body weight evolution revealed a progressive increase until maturity, followed by a decline during aging, with larger litters exhibiting lower
[...] Read more.
This study explores the multifaceted influence of litter size, maternal care, exercise, and aging on rats’ neurobehavioral plasticity and dentate gyrus microglia dynamics. Body weight evolution revealed a progressive increase until maturity, followed by a decline during aging, with larger litters exhibiting lower weights initially. Notably, exercised rats from smaller litters displayed higher body weights during the mature and aged stages. The dentate gyrus volumes showed no significant differences among groups, except for aged sedentary rats from smaller litters, which exhibited a reduction. Maternal care varied significantly based on litter size, with large litter dams showing lower frequencies of caregiving behaviors. Behavioral assays highlighted the detrimental impact of a sedentary lifestyle and reduced maternal care/large litters on spatial memory, mitigated by exercise in aged rats from smaller litters. The microglial dynamics in the layers of dentate gyrus revealed age-related changes modulated by litter size and exercise. Exercise interventions mitigated microgliosis associated with aging, particularly in aged rats. These findings underscore the complex interplay between early-life experiences, exercise, microglial dynamics, and neurobehavioral outcomes during aging.
Full article
(This article belongs to the Collection Collection on Systems Neuroscience)
►▼
Show Figures
Figure 1
Figure 1
<p>Timeline depicting the assessment of body weight and maternal care behaviors in pre- and post-weaned Wistar rats from small (6 pups/dam) and large litters (12/dam). M-Sed: mature adult rats leading a sedentary lifestyle; M-Ex: mature adult rats undergoing a regular exercise regimen; A-Sed: sedentary aged rats; A-Ex: aged rats undergoing a regular exercise regime.</p> Full article ">Figure 2
<p>Schematic representation of the experimental setups for object recognition tests. (<b>A</b>). Object identity recognition: rats are anticipated to exhibit a preference for the “novel” object over the “familiar” one. Purple shapes represent the “familiar” object; blue shape represents the “novel” object. (<b>B</b>). Object placement recognition: rats are anticipated to display a preference for the “displaced” object over the “stationary” one. Adapted from [<a href="#B54-brainsci-14-00497" class="html-bibr">54</a>].</p> Full article ">Figure 3
<p>Changes in body weight over time. This figure displays the mean body weight (±standard error of the mean) of rats raised in litters of 6 (small litters) and 12 (large litters), measured at different ages and under various exercise conditions. Statistical significance is indicated by asterisks: (*) denotes significant differences detected by a two-tailed <span class="html-italic">t</span>-test, covering the period from the 7th to the 90th postnatal day (PND). For the measurement taken at the 600th PND, significant differences were determined using a three-way ANOVA. The significance levels are as follows: (*) represents <span class="html-italic">p</span> < 0.05; (**) represents <span class="html-italic">p</span> < 0.01. These results highlight how body weight varies in rats depending on litter size and exercise over time.</p> Full article ">Figure 4
<p>Frequency of maternal behaviors exhibited by dams. (<b>A</b>): Average frequency of licking/grooming (LG) and arched-back nursing (ABN) per dam from small and large litters. Dams are categorized as high care (blue) or low care (red) based on their behavior frequency compared to the overall average ± standard deviation. Blue and red circles highlight high care and low care mothers, respectively, detected for each behavior. (<b>B</b>,<b>C</b>): Comparison of average LG and ABN behaviors among groups of dams from small and large litters. * <span class="html-italic">t</span>-test, <span class="html-italic">p</span> < 0.05.</p> Full article ">Figure 5
<p>Object recognition and placement analysis. (<b>A</b>) Object identity recognition: This section illustrates the results of testing how well different groups of rats recognize specific objects. (<b>B</b>) Object placement recognition: This section shows the outcomes of experiments assessing the ability of the same groups of rats to recognize changes in object placement. The groups are defined as follows: M-Sed: mature sedentary rats; M-Ex: mature exercised rats; A-Sed: aged sedentary rats; A-Ex: aged exercised rats. Statistical significance levels are indicated by asterisks: (*) represents a <span class="html-italic">p</span>-value of less than 0.05; (**) represents a <span class="html-italic">p</span>-value of less than 0.01; the results are based on two-tailed <span class="html-italic">t</span>-tests for related samples, illustrating the differences in performance across the experimental groups.</p> Full article ">Figure 6
<p>Laminar distribution of microglia in the dentate gyrus of Wistar rats. (<b>A</b>) Photomicrographs of immunolabeled sections from the dentate gyrus of mature (M) and aged (A) Wistar rats raised in either small (6 pups per dam) or large litters (12 pups per dam). These rats were subjected to a brief period of exercise (Ex) later in life or remained sedentary (Sed). The images show examples from rats with microglia counts close to the mean for each experimental group. The curved lines indicate the boundaries of the granular layer (GR), which lies between the molecular (MOL) and polymorphic (POL) layers. (<b>B</b>–<b>D</b>) The graphs display the mean microglial counts, with standard error bars, in the molecular (<b>B</b>), granular (<b>C</b>), and polymorphic (<b>D</b>) layers of the unilateral dentate gyrus. Significance levels are indicated as follows: (#, ## and ###) denote <span class="html-italic">p</span> < 0.05, <span class="html-italic">p</span> < 0.01 and <span class="html-italic">p</span> < 0.001, respectively, when comparing to mature rats; (*) indicates <span class="html-italic">p</span> < 0.05; (***) indicates <span class="html-italic">p</span> < 0.001. These significance levels represent outcomes from a three-way ANOVA.</p> Full article ">Figure 7
<p>Laminar redistribution of microglia in the rat dentate gyrus after early-life litter size changes and exercise. (<b>A</b>) This panel shows the percentage distribution of microglial counts within the polymorphic (POL), granular (GR), and molecular (MOL) layers of the rat dentate gyrus across various experimental conditions. This comparison assesses how early-life litter size changes and subsequent exercise impact microglial distribution. (<b>B</b>) This panel presents the absolute numbers of microglia in each layer, with the polymorphic layer at the top, the granular layer in the middle, and the molecular layer at the bottom. The data illustrate how microglial counts differ among groups with varying litter sizes and exercise conditions. The following abbreviations are used for the experimental groups: M: mature adult rats; A: aged rats; Sed-L: sedentary rats from large litters; Sed-S: sedentary rats from small litters; Ex-L: exercised rats from large litters; Ex-S: exercised rats from small litters. The asterisks indicate levels of statistical significance as follows: (*) denotes <span class="html-italic">p</span> < 0.05; (**) denotes <span class="html-italic">p</span> < 0.01. Significances were detected by 3-way ANOVA.</p> Full article ">
<p>Timeline depicting the assessment of body weight and maternal care behaviors in pre- and post-weaned Wistar rats from small (6 pups/dam) and large litters (12/dam). M-Sed: mature adult rats leading a sedentary lifestyle; M-Ex: mature adult rats undergoing a regular exercise regimen; A-Sed: sedentary aged rats; A-Ex: aged rats undergoing a regular exercise regime.</p> Full article ">Figure 2
<p>Schematic representation of the experimental setups for object recognition tests. (<b>A</b>). Object identity recognition: rats are anticipated to exhibit a preference for the “novel” object over the “familiar” one. Purple shapes represent the “familiar” object; blue shape represents the “novel” object. (<b>B</b>). Object placement recognition: rats are anticipated to display a preference for the “displaced” object over the “stationary” one. Adapted from [<a href="#B54-brainsci-14-00497" class="html-bibr">54</a>].</p> Full article ">Figure 3
<p>Changes in body weight over time. This figure displays the mean body weight (±standard error of the mean) of rats raised in litters of 6 (small litters) and 12 (large litters), measured at different ages and under various exercise conditions. Statistical significance is indicated by asterisks: (*) denotes significant differences detected by a two-tailed <span class="html-italic">t</span>-test, covering the period from the 7th to the 90th postnatal day (PND). For the measurement taken at the 600th PND, significant differences were determined using a three-way ANOVA. The significance levels are as follows: (*) represents <span class="html-italic">p</span> < 0.05; (**) represents <span class="html-italic">p</span> < 0.01. These results highlight how body weight varies in rats depending on litter size and exercise over time.</p> Full article ">Figure 4
<p>Frequency of maternal behaviors exhibited by dams. (<b>A</b>): Average frequency of licking/grooming (LG) and arched-back nursing (ABN) per dam from small and large litters. Dams are categorized as high care (blue) or low care (red) based on their behavior frequency compared to the overall average ± standard deviation. Blue and red circles highlight high care and low care mothers, respectively, detected for each behavior. (<b>B</b>,<b>C</b>): Comparison of average LG and ABN behaviors among groups of dams from small and large litters. * <span class="html-italic">t</span>-test, <span class="html-italic">p</span> < 0.05.</p> Full article ">Figure 5
<p>Object recognition and placement analysis. (<b>A</b>) Object identity recognition: This section illustrates the results of testing how well different groups of rats recognize specific objects. (<b>B</b>) Object placement recognition: This section shows the outcomes of experiments assessing the ability of the same groups of rats to recognize changes in object placement. The groups are defined as follows: M-Sed: mature sedentary rats; M-Ex: mature exercised rats; A-Sed: aged sedentary rats; A-Ex: aged exercised rats. Statistical significance levels are indicated by asterisks: (*) represents a <span class="html-italic">p</span>-value of less than 0.05; (**) represents a <span class="html-italic">p</span>-value of less than 0.01; the results are based on two-tailed <span class="html-italic">t</span>-tests for related samples, illustrating the differences in performance across the experimental groups.</p> Full article ">Figure 6
<p>Laminar distribution of microglia in the dentate gyrus of Wistar rats. (<b>A</b>) Photomicrographs of immunolabeled sections from the dentate gyrus of mature (M) and aged (A) Wistar rats raised in either small (6 pups per dam) or large litters (12 pups per dam). These rats were subjected to a brief period of exercise (Ex) later in life or remained sedentary (Sed). The images show examples from rats with microglia counts close to the mean for each experimental group. The curved lines indicate the boundaries of the granular layer (GR), which lies between the molecular (MOL) and polymorphic (POL) layers. (<b>B</b>–<b>D</b>) The graphs display the mean microglial counts, with standard error bars, in the molecular (<b>B</b>), granular (<b>C</b>), and polymorphic (<b>D</b>) layers of the unilateral dentate gyrus. Significance levels are indicated as follows: (#, ## and ###) denote <span class="html-italic">p</span> < 0.05, <span class="html-italic">p</span> < 0.01 and <span class="html-italic">p</span> < 0.001, respectively, when comparing to mature rats; (*) indicates <span class="html-italic">p</span> < 0.05; (***) indicates <span class="html-italic">p</span> < 0.001. These significance levels represent outcomes from a three-way ANOVA.</p> Full article ">Figure 7
<p>Laminar redistribution of microglia in the rat dentate gyrus after early-life litter size changes and exercise. (<b>A</b>) This panel shows the percentage distribution of microglial counts within the polymorphic (POL), granular (GR), and molecular (MOL) layers of the rat dentate gyrus across various experimental conditions. This comparison assesses how early-life litter size changes and subsequent exercise impact microglial distribution. (<b>B</b>) This panel presents the absolute numbers of microglia in each layer, with the polymorphic layer at the top, the granular layer in the middle, and the molecular layer at the bottom. The data illustrate how microglial counts differ among groups with varying litter sizes and exercise conditions. The following abbreviations are used for the experimental groups: M: mature adult rats; A: aged rats; Sed-L: sedentary rats from large litters; Sed-S: sedentary rats from small litters; Ex-L: exercised rats from large litters; Ex-S: exercised rats from small litters. The asterisks indicate levels of statistical significance as follows: (*) denotes <span class="html-italic">p</span> < 0.05; (**) denotes <span class="html-italic">p</span> < 0.01. Significances were detected by 3-way ANOVA.</p> Full article ">
Open AccessArticle
Frequent Lucid Dreaming Is Associated with Meditation Practice Styles, Meta-Awareness, and Trait Mindfulness
by
Elena Gerhardt and Benjamin Baird
Brain Sci. 2024, 14(5), 496; https://doi.org/10.3390/brainsci14050496 - 14 May 2024
Abstract
Lucid dreaming involves becoming aware that one’s current experience is a dream, which has similarities with the notion of mindfulness—becoming aware of moment-to-moment changes in experience. Additionally, meta-awareness, the ability to explicitly notice the current content of one’s own mental state, has also
[...] Read more.
Lucid dreaming involves becoming aware that one’s current experience is a dream, which has similarities with the notion of mindfulness—becoming aware of moment-to-moment changes in experience. Additionally, meta-awareness, the ability to explicitly notice the current content of one’s own mental state, has also been proposed to play an important role both in lucid dreaming and mindfulness meditation practices. However, research has shown conflicting strengths of associations between mindfulness, meditation, and lucid dreaming frequency, and the link between lucid dreaming and meta-awareness has not yet been empirically studied. This study evaluated the associations between lucid dreaming frequency and different meditation practice styles, mindfulness traits, and individual differences in meta-awareness through an online survey (n = 635). The results suggest that daily frequent meditators experience more lucid dreams than non-frequent meditators. However, weekly frequent meditators did not have a higher lucid dreaming frequency. A positive association was observed between open monitoring styles of meditation and lucid dreaming. The findings also indicate that meta-awareness is higher for meditators and weekly lucid dreamers. Furthermore, frequent lucid dreaming was commonly associated with a non-reactive stance and experiencing transcendence. Overall, the findings suggest a positive relationship between specific meditation practices and lucid dreaming as well as the importance of meta-awareness as a cognitive process linking meditation, mindfulness, and lucid dreaming.
Full article
(This article belongs to the Special Issue Recent Advances in Dreaming and Sleep-Related Metacognitions)
►▼
Show Figures
Figure 1
Figure 1
<p>Complete mediation model for the relationship between meditation frequency and the total number of lucid dreams in the previous six-month period with age, weekly dream recall, and monthly lucid dream induction frequencies as covariates. Note: <span class="html-italic">n</span> = 245. Bootstrapped BCa with R = 10,000. The model includes only standardized coefficients and effects; * = significant effect; grey dotted line = the insignificant direct effect; large dashed grey line = the insignificant effect of the covariate on the mediator; small grey dashed line = the effect of the covariate on the dependent variable; small dashed black lines = the significant effects of the covariates on the dependent variable; large dashed black lines = the significant effects of the covariates on the mediator; solid black lines = the indirect path.</p> Full article ">
<p>Complete mediation model for the relationship between meditation frequency and the total number of lucid dreams in the previous six-month period with age, weekly dream recall, and monthly lucid dream induction frequencies as covariates. Note: <span class="html-italic">n</span> = 245. Bootstrapped BCa with R = 10,000. The model includes only standardized coefficients and effects; * = significant effect; grey dotted line = the insignificant direct effect; large dashed grey line = the insignificant effect of the covariate on the mediator; small grey dashed line = the effect of the covariate on the dependent variable; small dashed black lines = the significant effects of the covariates on the dependent variable; large dashed black lines = the significant effects of the covariates on the mediator; solid black lines = the indirect path.</p> Full article ">
Open AccessArticle
Spatiotemporal Patterns of White Matter Maturation after Pre-Adolescence: A Diffusion Kurtosis Imaging Study
by
Ezequiel Farrher, Farida Grinberg, Tamara Khechiashvili, Irene Neuner, Kerstin Konrad and N. Jon Shah
Brain Sci. 2024, 14(5), 495; https://doi.org/10.3390/brainsci14050495 - 13 May 2024
Abstract
Diffusion tensor imaging (DTI) enables the assessment of changes in brain tissue microstructure during maturation and ageing. In general, patterns of cerebral maturation and decline render non-monotonic lifespan trajectories of DTI metrics with age, and, importantly, the rate of microstructural changes is heterochronous
[...] Read more.
Diffusion tensor imaging (DTI) enables the assessment of changes in brain tissue microstructure during maturation and ageing. In general, patterns of cerebral maturation and decline render non-monotonic lifespan trajectories of DTI metrics with age, and, importantly, the rate of microstructural changes is heterochronous for various white matter fibres. Recent studies have demonstrated that diffusion kurtosis imaging (DKI) metrics are more sensitive to microstructural changes during ageing compared to those of DTI. In a previous work, we demonstrated that the Cohen’s d of mean diffusional kurtosis (dMK) represents a useful biomarker for quantifying maturation heterochronicity. However, some inferences on the maturation grades of different fibre types, such as association, projection, and commissural, were of a preliminary nature due to the insufficient number of fibres considered. Hence, the purpose of this follow-up work was to further explore the heterochronicity of microstructural maturation between pre-adolescence and middle adulthood based on DTI and DKI metrics. Using the effect size of the between-group parametric changes and Cohen’s d, we observed that all commissural fibres achieved the highest level of maturity, followed by the majority of projection fibres, while the majority of association fibres were the least matured. We also demonstrated that dMK strongly correlates with the maxima or minima of the lifespan curves of DTI metrics. Furthermore, our results provide substantial evidence for the existence of spatial gradients in the timing of white matter maturation. In conclusion, our data suggest that DKI provides useful biomarkers for the investigation of maturation spatial heterogeneity and heterochronicity.
Full article
(This article belongs to the Section Neurotechnology and Neuroimaging)
►▼
Show Figures
Figure 1
Figure 1
<p>(<b>a</b>) Whole-brain histograms of the DT/KT metrics averaged within the groups of children and adults; (<b>b</b>) average histograms of the DT/KT metrics for three selected fibres: the Cg (AF), CST (PF), and GCC (CF).</p> Full article ">Figure 1 Cont.
<p>(<b>a</b>) Whole-brain histograms of the DT/KT metrics averaged within the groups of children and adults; (<b>b</b>) average histograms of the DT/KT metrics for three selected fibres: the Cg (AF), CST (PF), and GCC (CF).</p> Full article ">Figure 2
<p>(<b>a</b>) Relative changes in the DT parameters in adults and children for different fibres; (<b>b</b>) relative changes in the KT parameters in adults and children for different fibres. Significant between-group differences are indicated by asterisks based on the two-sided Student’s <span class="html-italic">t</span>-test analysis (<span class="html-italic">p</span> ≤ 0.00185, Bonferroni corrected).</p> Full article ">Figure 2 Cont.
<p>(<b>a</b>) Relative changes in the DT parameters in adults and children for different fibres; (<b>b</b>) relative changes in the KT parameters in adults and children for different fibres. Significant between-group differences are indicated by asterisks based on the two-sided Student’s <span class="html-italic">t</span>-test analysis (<span class="html-italic">p</span> ≤ 0.00185, Bonferroni corrected).</p> Full article ">Figure 3
<p>The values of <span class="html-italic">d</span><sub>MK</sub> for various fibres used to characterise the maturity of these fibres with respect to their microstructural changes between childhood and adulthood. The values are shown in descending order. The fibres with the highest <span class="html-italic">d</span><sub>MK</sub> are assumed to exhibit the most protracted maturation. AF, PF, and CF denote association, projection, and commissural fibres, respectively.</p> Full article ">Figure 4
<p>Scatter plots of <span class="html-italic">d</span><sub>MK</sub> versus age of peak for FA and age of minimum for MD for 10 investigated fibres. Herein, we used the data published by Lebel et al. [<a href="#B22-brainsci-14-00495" class="html-bibr">22</a>] for the age of peak and age of minimum. Pearson’s correlation coefficients, <span class="html-italic">r</span>, are indicated on the plots and provide evidence of strong correlations between <span class="html-italic">d</span><sub>MK</sub> and the age at which FA and MD reach their extreme values. ▼, ALIC; ◀, CST; *, GCC; ■, SCC; ★, BCC; ◆, FCB; ●, Cg; ✶, SFOF; ▶, SLF; ▲, UF.</p> Full article ">Figure 5
<p>(<b>a</b>) Profiles of <math display="inline"><semantics> <mrow> <mfenced open="〈" close="〉" separators="|"> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </mfenced> </mrow> </semantics></math>, where <span class="html-italic">A</span> denotes one of the DT parameters indicated at the vertical axes; (<b>b</b>) profiles of <math display="inline"><semantics> <mrow> <mfenced open="〈" close="〉" separators="|"> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </mfenced> </mrow> </semantics></math>, where <span class="html-italic">A</span> denotes one of the KT parameters indicated at the vertical axes. All profiles appear “centred” around zero since the mean values of <math display="inline"><semantics> <mrow> <mfenced open="〈" close="〉" separators="|"> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </mfenced> </mrow> </semantics></math> averaged over the entire profile were subtracted from the ordinates (to simplify visualisation). The colour of individual data points indicates whether the <span class="html-italic">t</span>-test of between-group differences performed for the values of <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </semantics></math> (considering only skeleton voxels of the corresponding plane) was significant (red) or non-significant (blue) with (threshold) α values set to 0.05.</p> Full article ">Figure 5 Cont.
<p>(<b>a</b>) Profiles of <math display="inline"><semantics> <mrow> <mfenced open="〈" close="〉" separators="|"> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </mfenced> </mrow> </semantics></math>, where <span class="html-italic">A</span> denotes one of the DT parameters indicated at the vertical axes; (<b>b</b>) profiles of <math display="inline"><semantics> <mrow> <mfenced open="〈" close="〉" separators="|"> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </mfenced> </mrow> </semantics></math>, where <span class="html-italic">A</span> denotes one of the KT parameters indicated at the vertical axes. All profiles appear “centred” around zero since the mean values of <math display="inline"><semantics> <mrow> <mfenced open="〈" close="〉" separators="|"> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </mfenced> </mrow> </semantics></math> averaged over the entire profile were subtracted from the ordinates (to simplify visualisation). The colour of individual data points indicates whether the <span class="html-italic">t</span>-test of between-group differences performed for the values of <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </semantics></math> (considering only skeleton voxels of the corresponding plane) was significant (red) or non-significant (blue) with (threshold) α values set to 0.05.</p> Full article ">
<p>(<b>a</b>) Whole-brain histograms of the DT/KT metrics averaged within the groups of children and adults; (<b>b</b>) average histograms of the DT/KT metrics for three selected fibres: the Cg (AF), CST (PF), and GCC (CF).</p> Full article ">Figure 1 Cont.
<p>(<b>a</b>) Whole-brain histograms of the DT/KT metrics averaged within the groups of children and adults; (<b>b</b>) average histograms of the DT/KT metrics for three selected fibres: the Cg (AF), CST (PF), and GCC (CF).</p> Full article ">Figure 2
<p>(<b>a</b>) Relative changes in the DT parameters in adults and children for different fibres; (<b>b</b>) relative changes in the KT parameters in adults and children for different fibres. Significant between-group differences are indicated by asterisks based on the two-sided Student’s <span class="html-italic">t</span>-test analysis (<span class="html-italic">p</span> ≤ 0.00185, Bonferroni corrected).</p> Full article ">Figure 2 Cont.
<p>(<b>a</b>) Relative changes in the DT parameters in adults and children for different fibres; (<b>b</b>) relative changes in the KT parameters in adults and children for different fibres. Significant between-group differences are indicated by asterisks based on the two-sided Student’s <span class="html-italic">t</span>-test analysis (<span class="html-italic">p</span> ≤ 0.00185, Bonferroni corrected).</p> Full article ">Figure 3
<p>The values of <span class="html-italic">d</span><sub>MK</sub> for various fibres used to characterise the maturity of these fibres with respect to their microstructural changes between childhood and adulthood. The values are shown in descending order. The fibres with the highest <span class="html-italic">d</span><sub>MK</sub> are assumed to exhibit the most protracted maturation. AF, PF, and CF denote association, projection, and commissural fibres, respectively.</p> Full article ">Figure 4
<p>Scatter plots of <span class="html-italic">d</span><sub>MK</sub> versus age of peak for FA and age of minimum for MD for 10 investigated fibres. Herein, we used the data published by Lebel et al. [<a href="#B22-brainsci-14-00495" class="html-bibr">22</a>] for the age of peak and age of minimum. Pearson’s correlation coefficients, <span class="html-italic">r</span>, are indicated on the plots and provide evidence of strong correlations between <span class="html-italic">d</span><sub>MK</sub> and the age at which FA and MD reach their extreme values. ▼, ALIC; ◀, CST; *, GCC; ■, SCC; ★, BCC; ◆, FCB; ●, Cg; ✶, SFOF; ▶, SLF; ▲, UF.</p> Full article ">Figure 5
<p>(<b>a</b>) Profiles of <math display="inline"><semantics> <mrow> <mfenced open="〈" close="〉" separators="|"> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </mfenced> </mrow> </semantics></math>, where <span class="html-italic">A</span> denotes one of the DT parameters indicated at the vertical axes; (<b>b</b>) profiles of <math display="inline"><semantics> <mrow> <mfenced open="〈" close="〉" separators="|"> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </mfenced> </mrow> </semantics></math>, where <span class="html-italic">A</span> denotes one of the KT parameters indicated at the vertical axes. All profiles appear “centred” around zero since the mean values of <math display="inline"><semantics> <mrow> <mfenced open="〈" close="〉" separators="|"> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </mfenced> </mrow> </semantics></math> averaged over the entire profile were subtracted from the ordinates (to simplify visualisation). The colour of individual data points indicates whether the <span class="html-italic">t</span>-test of between-group differences performed for the values of <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </semantics></math> (considering only skeleton voxels of the corresponding plane) was significant (red) or non-significant (blue) with (threshold) α values set to 0.05.</p> Full article ">Figure 5 Cont.
<p>(<b>a</b>) Profiles of <math display="inline"><semantics> <mrow> <mfenced open="〈" close="〉" separators="|"> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </mfenced> </mrow> </semantics></math>, where <span class="html-italic">A</span> denotes one of the DT parameters indicated at the vertical axes; (<b>b</b>) profiles of <math display="inline"><semantics> <mrow> <mfenced open="〈" close="〉" separators="|"> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </mfenced> </mrow> </semantics></math>, where <span class="html-italic">A</span> denotes one of the KT parameters indicated at the vertical axes. All profiles appear “centred” around zero since the mean values of <math display="inline"><semantics> <mrow> <mfenced open="〈" close="〉" separators="|"> <mrow> <mi mathvariant="sans-serif">Δ</mi> <msub> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </mfenced> </mrow> </semantics></math> averaged over the entire profile were subtracted from the ordinates (to simplify visualisation). The colour of individual data points indicates whether the <span class="html-italic">t</span>-test of between-group differences performed for the values of <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi>A</mi> </mrow> <mo>¯</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">v</mi> </mrow> </msub> </mrow> </semantics></math> (considering only skeleton voxels of the corresponding plane) was significant (red) or non-significant (blue) with (threshold) α values set to 0.05.</p> Full article ">
Open AccessArticle
Assessing the Relationship between Surgical Timing and Postoperative Seizure Outcomes in Cavernoma-Related Epilepsy: A Single-Institution Retrospective Analysis of 63 Patients with a Review of the Literature
by
Elsa Nico, Christopher O. Adereti, Ashia M. Hackett, Andrea Bianconi, Anant Naik, Adam T. Eberle, Pere J. Cifre Serra, Stefan W. Koester, Samuel L. Malnik, Brandon M. Fox, Joelle N. Hartke, Ethan A. Winkler, Joshua S. Catapano and Michael T. Lawton
Brain Sci. 2024, 14(5), 494; https://doi.org/10.3390/brainsci14050494 - 13 May 2024
Abstract
Background: Patients with supratentorial cavernous malformations (SCMs) commonly present with seizures. First-line treatments for cavernoma-related epilepsy (CRE) include conservative management (antiepileptic drugs (AEDs)) and surgery. We compared seizure outcomes of CRE patients after early (≤6 months) vs. delayed (>6 months) surgery. Methods
[...] Read more.
Background: Patients with supratentorial cavernous malformations (SCMs) commonly present with seizures. First-line treatments for cavernoma-related epilepsy (CRE) include conservative management (antiepileptic drugs (AEDs)) and surgery. We compared seizure outcomes of CRE patients after early (≤6 months) vs. delayed (>6 months) surgery. Methods: We compared outcomes of CRE patients with SCMs surgically treated at our large-volume cerebrovascular center (1 January 2010–31 July 2020). Patients with 1 sporadic SCM and ≥1-year follow-up were included. Primary outcomes were International League Against Epilepsy (ILAE) class 1 seizure freedom and AED independence. Results: Of 63 CRE patients (26 women, 37 men; mean ± SD age, 36.1 ± 14.6 years), 48 (76%) vs. 15 (24%) underwent early (mean ± SD, 2.1 ± 1.7 months) vs. delayed (mean ± SD, 6.2 ± 7.1 years) surgery. Most (32 (67%)) with early surgery presented after 1 seizure; all with delayed surgery had ≥2 seizures. Seven (47%) with delayed surgery had drug-resistant epilepsy. At follow-up (mean ± SD, 5.4 ± 3.3 years), CRE patients with early surgery were more likely to have ILAE class 1 seizure freedom and AED independence than those with delayed surgery (92% (44/48) vs. 53% (8/15), p = 0.002; and 65% (31/48) vs. 33% (5/15), p = 0.03, respectively). Conclusions: Early CRE surgery demonstrated better seizure outcomes than delayed surgery. Multicenter prospective studies are needed to validate these findings.
Full article
(This article belongs to the Special Issue Cerebrovascular Neurosurgery)
►▼
Show Figures
Figure 1
Figure 1
<p>Patient selection flow diagram. <span class="html-italic">Used with permission from Barrow Neurological Institute, Phoenix, Arizona</span>.</p> Full article ">Figure 2
<p>Literature selection flow diagram. <span class="html-italic">Used with permission from Barrow Neurological Institute, Phoenix, Arizona</span>.</p> Full article ">
<p>Patient selection flow diagram. <span class="html-italic">Used with permission from Barrow Neurological Institute, Phoenix, Arizona</span>.</p> Full article ">Figure 2
<p>Literature selection flow diagram. <span class="html-italic">Used with permission from Barrow Neurological Institute, Phoenix, Arizona</span>.</p> Full article ">
Journal Menu
► ▼ Journal Menu-
- Brain Sciences Home
- Aims & Scope
- Editorial Board
- Reviewer Board
- Topical Advisory Panel
- Instructions for Authors
- Special Issues
- Topics
- Sections & Collections
- Article Processing Charge
- Indexing & Archiving
- Editor’s Choice Articles
- Most Cited & Viewed
- Journal Statistics
- Journal History
- Journal Awards
- Society Collaborations
- Conferences
- Editorial Office
Journal Browser
► ▼ Journal BrowserHighly Accessed Articles
Latest Books
E-Mail Alert
News
Topics
Topic in
Brain Sciences, Healthcare, Informatics, IJERPH
Applications of Virtual Reality Technology in Rehabilitation
Topic Editors: Jorge Oliveira, Pedro GamitoDeadline: 30 June 2024
Topic in
Brain Sciences, Clinics and Practice, COVID, Life, Vaccines, Viruses
Multifaceted Efforts from Basic Research to Clinical Practice in Controlling COVID-19 Disease
Topic Editors: Yih-Horng Shiao, Rashi OjhaDeadline: 30 September 2024
Topic in
Biomedicines, Brain Sciences, Geriatrics, Life, Neurology International
Translational Advances in Neurodegenerative Dementias
Topic Editors: Francesco Di Lorenzo, Annibale AntonioniDeadline: 31 October 2024
Topic in
Biomedicines, Brain Sciences, Cells, IJMS, JCM
Applications of Biomedical Technology and Molecular Biological Approach in Brain Diseases
Topic Editors: Andrew Chih Wei Huang, Seong Soo A. An, Bai Chuang Shyu, Muh-Shi Lin, Anna KozłowskaDeadline: 31 December 2024
Conferences
Special Issues
Special Issue in
Brain Sciences
Virtual Reality and Gamified Applications as Therapeutic Tools in Mental Health
Guest Editor: Oana Alexandra DavidDeadline: 20 May 2024
Special Issue in
Brain Sciences
Advances in Invasive and Non-invasive Brain Stimulation in Movement Disorders
Guest Editors: Jianguo Zhang, Wei Hu, Fangang MengDeadline: 7 June 2024
Special Issue in
Brain Sciences
Sleep, Circadian Rhythms and Cognitive Function
Guest Editor: Maria Comas SoberatsDeadline: 10 June 2024
Special Issue in
Brain Sciences
Recent Advances in Central Nervous System Multiscale Imaging
Guest Editors: Michela Fratini, Alejandra Sierra LopezDeadline: 30 June 2024
Topical Collections
Topical Collection in
Brain Sciences
Collection on Developmental Neuroscience
Collection Editor: Mark Burke
Topical Collection in
Brain Sciences
Systematic Reviews and Meta-Analyses Collection on Molecular and Cellular Neuroscience
Collection Editor: Andrew Clarkson
Topical Collection in
Brain Sciences
Primary Progressive Aphasia and Apraxia of Speech
Collection Editors: Jordi A. Matias-Guiu, Robert Jr Laforce, Rene L. Utianski
Topical Collection in
Brain Sciences
Human Ultrasound Neuromodulation: State of the Art
Collection Editor: Roland Beisteiner