Pub Date : 2025-09-01Epub Date: 2025-06-11DOI: 10.1016/j.ynirp.2025.100267
Matthew Toews , Talía Vázquez Romaguera , William Wells III , Nikos Makris
This paper investigates the link between sex and the human brain from anatomical MRI data, where a primary confound is the size difference between male and female groups. Anatomy is characterized by the 3D scale-invariant feature transform (SIFT), where features are salient image regions that are automatically identified and normalized according local size or scale. Experiments use T1-w MRI data of 422 healthy unrelated subjects from the Human Connectome Project (HCP) dataset (211 males, 211 females, 22–36 years of age). We found that brain sex may be predicted via image-to-image feature matching with 91.9% accuracy, that classification is driven largely by weakly-informative features distributed throughout the brain and shared by unique subsets of subjects, and that a pair of features identified in the right and left thalamic regions of 97% of subjects predicts sex with 74% accuracy. Misclassified subjects exhibit features typical of the opposite sex in one or both hemispheres.
{"title":"Representative scale-invariant characteristics of male and female brains in magnetic resonance images","authors":"Matthew Toews , Talía Vázquez Romaguera , William Wells III , Nikos Makris","doi":"10.1016/j.ynirp.2025.100267","DOIUrl":"10.1016/j.ynirp.2025.100267","url":null,"abstract":"<div><div>This paper investigates the link between sex and the human brain from anatomical MRI data, where a primary confound is the size difference between male and female groups. Anatomy is characterized by the 3D scale-invariant feature transform (SIFT), where features are salient image regions that are automatically identified and normalized according local size or scale. Experiments use T1-w MRI data of 422 healthy unrelated subjects from the Human Connectome Project (HCP) dataset (211 males, 211 females, 22–36 years of age). We found that brain sex may be predicted via image-to-image feature matching with 91.9% accuracy, that classification is driven largely by weakly-informative features distributed throughout the brain and shared by unique subsets of subjects, and that a pair of features identified in the right and left thalamic regions of 97% of subjects predicts sex with 74% accuracy. Misclassified subjects exhibit features typical of the opposite sex in one or both hemispheres.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"5 3","pages":"Article 100267"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144255194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-06-28DOI: 10.1016/j.ynirp.2025.100278
Tahereh Rashnavadi , Raphael F. Casseb , Kristine E. Woodward , Paolo Federico , Bradley Goodyear
Frontal lobe epilepsy (FLE), marked by recurrent seizures arising from the frontal lobes, can significantly impair cognitive and motor function, reducing quality of life. Recent studies suggest that epilepsies can involve functional networks throughout the brain that can be identified using resting-state functional magnetic resonance imaging (fMRI). In this study, we aimed to determine whether FLE is associated with a distinct functional network brain states. Using dynamic functional connectivity analysis in combination with k-means clustering, we investigated dynamic connectivity patterns of the somatomotor network (SMN) and default mode network (DMN) of ten right-hemisphere and six left-hemisphere FLE patients, as well as nine healthy controls. We found two distinct states of rest for both the SMN and DMN: a high connectivity state and a lower, more variable connectivity state that was often specific to individual patients. Both FLE groups showed reduced overall connectivity compared to controls, with the greatest differences emerging during the low connectivity state. Right FLE patients and controls exhibited relatively uniform reductions, whereas left FLE patients showed spatially specific disruptions, including reduced lateral-to-medial SMN connectivity and decreased connectivity in posterior and left-lateralized DMN regions. Our findings suggest that dynamic connectivity analysis can uncover the temporal complexity and patient-specific nature of brain network disruption in FLE, supporting the development of personalized diagnostic and treatment strategies. Further research with larger cohorts is necessary to validate these results and explore additional factors affecting brain functional connectivity.
{"title":"Motor and default mode network states of rest in frontal lobe epilepsy","authors":"Tahereh Rashnavadi , Raphael F. Casseb , Kristine E. Woodward , Paolo Federico , Bradley Goodyear","doi":"10.1016/j.ynirp.2025.100278","DOIUrl":"10.1016/j.ynirp.2025.100278","url":null,"abstract":"<div><div>Frontal lobe epilepsy (FLE), marked by recurrent seizures arising from the frontal lobes, can significantly impair cognitive and motor function, reducing quality of life. Recent studies suggest that epilepsies can involve functional networks throughout the brain that can be identified using resting-state functional magnetic resonance imaging (fMRI). In this study, we aimed to determine whether FLE is associated with a distinct functional network brain states. Using dynamic functional connectivity analysis in combination with <em>k</em>-means clustering, we investigated dynamic connectivity patterns of the somatomotor network (SMN) and default mode network (DMN) of ten right-hemisphere and six left-hemisphere FLE patients, as well as nine healthy controls. We found two distinct states of rest for both the SMN and DMN: a high connectivity state and a lower, more variable connectivity state that was often specific to individual patients. Both FLE groups showed reduced overall connectivity compared to controls, with the greatest differences emerging during the low connectivity state. Right FLE patients and controls exhibited relatively uniform reductions, whereas left FLE patients showed spatially specific disruptions, including reduced lateral-to-medial SMN connectivity and decreased connectivity in posterior and left-lateralized DMN regions. Our findings suggest that dynamic connectivity analysis can uncover the temporal complexity and patient-specific nature of brain network disruption in FLE, supporting the development of personalized diagnostic and treatment strategies. Further research with larger cohorts is necessary to validate these results and explore additional factors affecting brain functional connectivity.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"5 3","pages":"Article 100278"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-06-25DOI: 10.1016/j.ynirp.2025.100276
Erin L. Meier , Lisa D. Bunker , Hana Kim , Alexandra Zezinka Durfee , Victoria Tilton-Bolowsky , Voss Neal , Argye E. Hillis
Functional Near-Infrared Spectroscopy (fNIRS) is an emerging neurotechnology that has several advantages over fMRI, but questions remain about factors that affect data quality and activity in stroke survivors. We examined the effect of protocol factors (Aim 1) and participant characteristics (Aim 2) on raw fNIRS signal quality and tested associations between quality control metrics and brain activity and connectivity (Aim 3) in a sample of 107 individuals with a history of left or right hemisphere stroke. Participants completed tasks that varied by cognitive and motor speech demands (from low to high): Resting State, Discourse Comprehension, and Picture Naming. Scalp-coupling indices, peak spectral power values, and number of bad channels from each task were extracted from the Quality Testing of Near Infrared Scans (QT-NIRS) toolbox (Montero-Hernandez and Pollonini, 2020) and used to index raw data quality. Data quality did not vary by session location or protocol experience, but all data quality metrics from Picture Naming were significantly lower than those from the other tasks. fNIRS signals were generally worse for Black women compared to Black men and White individuals regardless of gender. No significant associations between the raw fNIRS signal quality and Resting State functional connectivity were found. However, relative changes in Picture Naming hemoglobin concentrations were associated with scalp-coupling indices for certain channels. These results highlight the need for careful data preprocessing of already collected data and a systematic approach in future studies to mitigate inherent biases of optical instruments, thereby enhancing the inclusion of underrepresented groups in neuroscience research.
功能近红外光谱（fNIRS）是一种新兴的神经技术，与功能磁共振成像相比有许多优势，但影响中风幸存者数据质量和活动的因素仍然存在问题。我们研究了方案因素（Aim 1）和参与者特征（Aim 2）对原始fNIRS信号质量的影响，并测试了质量控制指标与大脑活动和连通性（Aim 3）之间的关联，样本包括107名有左半球或右半球卒中史的个体。参与者完成了由认知和运动语言需求（从低到高）不同的任务：静息状态、话语理解和图片命名。从近红外扫描质量测试（QT-NIRS）工具箱（Montero-Hernandez and Pollonini, 2020）中提取每个任务的头皮耦合指数、峰值光谱功率值和坏通道数量，并用于索引原始数据质量。数据质量不会因会话位置或协议经验而变化，但是来自图片命名的所有数据质量指标明显低于来自其他任务的数据质量指标。与黑人男性和白人相比，黑人女性的近红外光谱信号普遍较差，无论性别如何。未发现原始fNIRS信号质量与静息状态功能连接之间存在显著关联。然而，图像命名血红蛋白浓度的相对变化与某些通道的头皮偶联指数有关。这些结果强调了在未来的研究中需要对已经收集的数据进行仔细的数据预处理，并采用系统的方法来减轻光学仪器的固有偏差，从而增强神经科学研究中代表性不足群体的纳入。
{"title":"The effects of protocol factors and participant characteristics on functional near-infrared spectroscopy data quality after stroke","authors":"Erin L. Meier , Lisa D. Bunker , Hana Kim , Alexandra Zezinka Durfee , Victoria Tilton-Bolowsky , Voss Neal , Argye E. Hillis","doi":"10.1016/j.ynirp.2025.100276","DOIUrl":"10.1016/j.ynirp.2025.100276","url":null,"abstract":"<div><div>Functional Near-Infrared Spectroscopy (fNIRS) is an emerging neurotechnology that has several advantages over fMRI, but questions remain about factors that affect data quality and activity in stroke survivors. We examined the effect of protocol factors (Aim 1) and participant characteristics (Aim 2) on raw fNIRS signal quality and tested associations between quality control metrics and brain activity and connectivity (Aim 3) in a sample of 107 individuals with a history of left or right hemisphere stroke. Participants completed tasks that varied by cognitive and motor speech demands (from low to high): Resting State, Discourse Comprehension, and Picture Naming. Scalp-coupling indices, peak spectral power values, and number of bad channels from each task were extracted from the Quality Testing of Near Infrared Scans (QT-NIRS) toolbox (Montero-Hernandez and Pollonini, 2020) and used to index raw data quality. Data quality did not vary by session location or protocol experience, but all data quality metrics from Picture Naming were significantly lower than those from the other tasks. fNIRS signals were generally worse for Black women compared to Black men and White individuals regardless of gender. No significant associations between the raw fNIRS signal quality and Resting State functional connectivity were found. However, relative changes in Picture Naming hemoglobin concentrations were associated with scalp-coupling indices for certain channels. These results highlight the need for careful data preprocessing of already collected data and a systematic approach in future studies to mitigate inherent biases of optical instruments, thereby enhancing the inclusion of underrepresented groups in neuroscience research.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"5 3","pages":"Article 100276"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01Epub Date: 2025-08-01DOI: 10.1016/j.ynirp.2025.100280
Camilo J. Cela-Conde , Sara Lumbreras , Sandra Pusil , Brenda Chino , José M. Caamaño , Laura Gismera , Fernando Maestú , Luis Rojas-Marcos
According to the standard definition, a creative act requires originality and effectiveness. Creativity is widely considered an exclusively human characteristic, linked to the activity of brain networks such as the Default Mode Network (DMN), the Fronto-Parietal Network (FPN), and, to a lesser extent, the Salience Network (SN). A significant body of literature explores the viability of teaching creativity, often reporting positive results. However, little attention has been paid to the neural network modifications induced by creativity training.
This study investigates changes of creativity-related brain networks over time in the resting state (participants without specific cognitive activities). The stages considered were before and after a learning process focused on visual aesthetic creation tasks (Gabarron Method). High-density electroencephalography (EEG) was used to record brain activity. 51 female volunteers participated in the research.
The results show a significant increase in the activation of the DMN and FPN, with a more limited effect in the SN. The DMN and FPN are neural networks commonly activated during artistic creation and aesthetic perception tasks. This finding supports the existence of what could be called a 'creative universe,' encompassing capacities such as creation, perception, and divergent thinking.
{"title":"Teaching-induced changes in neural networks: Toward a model of the creative universe","authors":"Camilo J. Cela-Conde , Sara Lumbreras , Sandra Pusil , Brenda Chino , José M. Caamaño , Laura Gismera , Fernando Maestú , Luis Rojas-Marcos","doi":"10.1016/j.ynirp.2025.100280","DOIUrl":"10.1016/j.ynirp.2025.100280","url":null,"abstract":"<div><div>According to the standard definition, a creative act requires originality and effectiveness. Creativity is widely considered an exclusively human characteristic, linked to the activity of brain networks such as the Default Mode Network (DMN), the Fronto-Parietal Network (FPN), and, to a lesser extent, the Salience Network (SN). A significant body of literature explores the viability of teaching creativity, often reporting positive results. However, little attention has been paid to the neural network modifications induced by creativity training.</div><div>This study investigates changes of creativity-related brain networks over time in the resting state (participants without specific cognitive activities). The stages considered were before and after a learning process focused on visual aesthetic creation tasks (Gabarron Method). High-density electroencephalography (EEG) was used to record brain activity. 51 female volunteers participated in the research.</div><div>The results show a significant increase in the activation of the DMN and FPN, with a more limited effect in the SN. The DMN and FPN are neural networks commonly activated during artistic creation and aesthetic perception tasks. This finding supports the existence of what could be called a 'creative universe,' encompassing capacities such as creation, perception, and divergent thinking.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"5 3","pages":"Article 100280"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144749338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-14DOI: 10.1016/j.ynirp.2025.100263
Leanne Tamm , Jonathan A. Dudley , Sarah L. Karalunas , John O. Simon , Thomas C. Maloney , Gowtham Atluri , Jeffery N. Epstein
Patients with ADHD evidence elevated reaction time variability (RTV) due to periodic long reaction times (RTs). Even though reaction time variability (RTV) reflects intraindividual differences in RT across time, prior research exploring the neural basis of RTV in ADHD has primarily examined associations between neural activation and summary RTV outcomes (e.g., standard deviation of reaction time, tau). Here, we explore group differences in the neural basis of RTV by examining association between trial-level RTs and fMRI BOLD activation obtained during a Stop Signal Task in a large (n = 5719) sample of 9- to 10-year-old children participating in the Adolescent Brain Cognitive Development (ABCD) study. Children with ADHD demonstrated greater RTV than those without ADHD. ADHD-related group differences were not observed between fMRI BOLD activation and summary RTV outcomes. At the trial level, longer RTs were associated with increased BOLD activation in salience/ventral attention and executive control networks and decreased BOLD activation in the default mode network, consistent with time-on-task effects (i.e., stimulus processing time) in which long RTs require maintaining task-positive activation and DMN suppression for more time than short RTs. Moreover, children with ADHD showed weaker associations between long RTs and BOLD activation in these regions than children without ADHD supporting models that point to dysregulated competition between the DMN and executive network as mechanism of cognitive impairment in ADHD.
{"title":"Exploring the neural basis of reaction time variability in ADHD: The importance of examining data at the trial level","authors":"Leanne Tamm , Jonathan A. Dudley , Sarah L. Karalunas , John O. Simon , Thomas C. Maloney , Gowtham Atluri , Jeffery N. Epstein","doi":"10.1016/j.ynirp.2025.100263","DOIUrl":"10.1016/j.ynirp.2025.100263","url":null,"abstract":"<div><div>Patients with ADHD evidence elevated reaction time variability (RTV) due to periodic long reaction times (RTs). Even though reaction time variability (RTV) reflects intraindividual differences in RT across time, prior research exploring the neural basis of RTV in ADHD has primarily examined associations between neural activation and summary RTV outcomes (e.g., standard deviation of reaction time, tau). Here, we explore group differences in the neural basis of RTV by examining association between trial-level RTs and fMRI BOLD activation obtained during a Stop Signal Task in a large (<em>n</em> = 5719) sample of 9- to 10-year-old children participating in the Adolescent Brain Cognitive Development (ABCD) study. Children with ADHD demonstrated greater RTV than those without ADHD. ADHD-related group differences were not observed between fMRI BOLD activation and summary RTV outcomes. At the trial level, longer RTs were associated with increased BOLD activation in salience/ventral attention and executive control networks and decreased BOLD activation in the default mode network, consistent with time-on-task effects (i.e., stimulus processing time) in which long RTs require maintaining task-positive activation and DMN suppression for more time than short RTs. Moreover, children with ADHD showed weaker associations between long RTs and BOLD activation in these regions than children without ADHD supporting models that point to dysregulated competition between the DMN and executive network as mechanism of cognitive impairment in ADHD.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"5 2","pages":"Article 100263"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-19DOI: 10.1016/j.ynirp.2025.100262
Alex M. Pagnozzi , Kerstin Pannek , Roslyn N. Boyd , Liza van Eijk , Joanne M. George , Samudragupta Bora , DanaKai Bradford , Michael Fahey , Michael Ditchfield , Atul Malhotra , Paul B. Colditz , Jurgen Fripp
Brain Magnetic Resonance Imaging (MRI) of high-risk infants in the neonatal period (from 26 weeks postmenstrual age to Term Equivalent Age (TEA)) is increasingly being used for the detection of brain injuries, and the early prognostication of adverse outcomes such as Cerebral Palsy (CP). While most imaging is performed around TEA in clinical practice for infants born preterm (<37 weeks of gestation), this would often require families to return to hospital for imaging. In this work, we extract structural biomarkers from MRI acquired both before and at TEA in a cohort of very preterm infants from the PPREMO and PREBO studies (n = 100), to determine if either time-point, or both combined, are predictive of both Bayley Scales of Infant and Toddler Development – Third Edition (Bayley-III) and the Neuro-sensory Motor Developmental Assessment (NSMDA) at 2 years. Using multivariable regression, moderately strong and statistically significant associations were found between brain structure on both early and TEA MRIs with 2-year outcomes (r = 0.39–0.55 for early MRI, r = 0.37–0.49 for Term MRI, r = 0.37–0.56 for early and TEA MRI combined). Importantly, brain biomarkers associated with early childhood outcomes from MRIs were identified, including white and grey matter volumes, deep grey matter and cerebellar volumes, and gyrification and surface area across the whole cortex. Early MRI showed the best prognostic accuracy along with combining timepoints, indicating the potential clinical benefit of Early MRI in predicting adverse outcomes.
新生儿期高危婴儿（经后26周至足月年龄（TEA））的脑磁共振成像（MRI）越来越多地被用于脑损伤的检测和不良后果的早期预测，如脑瘫（CP）。虽然在临床实践中，大多数影像学检查是在TEA前后对早产婴儿（妊娠37周）进行的，但这通常需要家庭返回医院进行影像学检查。在这项工作中，我们从PPREMO和PREBO研究的极早产儿队列（n = 100）中提取了在TEA前和TEA时获得的MRI结构生物标志物，以确定任何一个时间点，或两者结合，是否可以预测2岁时的Bayley婴幼儿发育量表-第三版（Bayley- iii）和神经感觉运动发育评估（NSMDA）。通过多变量回归，发现早期和TEA MRI的脑结构与2年预后之间存在中等强且有统计学意义的关联（早期MRI r = 0.39-0.55, Term MRI r = 0.37-0.49，早期和TEA MRI合并r = 0.37-0.56）。重要的是，从mri中确定了与早期儿童结果相关的大脑生物标志物，包括白质和灰质体积、深灰质和小脑体积，以及整个皮层的回转和表面积。早期MRI在联合时间点上显示出最好的预后准确性，这表明早期MRI在预测不良结局方面具有潜在的临床益处。
{"title":"Brain MRI before and at term equivalent age predicts motor and cognitive outcomes in very preterm infants","authors":"Alex M. Pagnozzi , Kerstin Pannek , Roslyn N. Boyd , Liza van Eijk , Joanne M. George , Samudragupta Bora , DanaKai Bradford , Michael Fahey , Michael Ditchfield , Atul Malhotra , Paul B. Colditz , Jurgen Fripp","doi":"10.1016/j.ynirp.2025.100262","DOIUrl":"10.1016/j.ynirp.2025.100262","url":null,"abstract":"<div><div>Brain Magnetic Resonance Imaging (MRI) of high-risk infants in the neonatal period (from 26 weeks postmenstrual age to Term Equivalent Age (TEA)) is increasingly being used for the detection of brain injuries, and the early prognostication of adverse outcomes such as Cerebral Palsy (CP). While most imaging is performed around TEA in clinical practice for infants born preterm (<37 weeks of gestation), this would often require families to return to hospital for imaging. In this work, we extract structural biomarkers from MRI acquired both before and at TEA in a cohort of very preterm infants from the PPREMO and PREBO studies (n = 100), to determine if either time-point, or both combined, are predictive of both Bayley Scales of Infant and Toddler Development – Third Edition (Bayley-III) and the Neuro-sensory Motor Developmental Assessment (NSMDA) at 2 years. Using multivariable regression, moderately strong and statistically significant associations were found between brain structure on both early and TEA MRIs with 2-year outcomes (r = 0.39–0.55 for early MRI, r = 0.37–0.49 for Term MRI, r = 0.37–0.56 for early and TEA MRI combined). Importantly, brain biomarkers associated with early childhood outcomes from MRIs were identified, including white and grey matter volumes, deep grey matter and cerebellar volumes, and gyrification and surface area across the whole cortex. Early MRI showed the best prognostic accuracy along with combining timepoints, indicating the potential clinical benefit of Early MRI in predicting adverse outcomes.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"5 2","pages":"Article 100262"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-03-23DOI: 10.1016/j.ynirp.2025.100255
Jenna K. Blujus , Michael W. Cole , Elena K. Festa , Stephen L. Buka , Stephen P. Salloway , William C. Heindel , Hwamee Oh , the Alzheimer's Disease Neuroimaging Initiative
Several neural mechanisms underlying resilience to Alzheimer's disease (AD) have been proposed, including redundant neural connections between the posterior hippocampi and all other brain regions, and global functional connectivity of the left frontal cortex (LFC). Here, we investigated if functional redundancy of the hippocampus (HC) and LFC underscores neural resilience in the presence of early AD pathologies. From the ADNI database, cognitively normal older adults (CN) (N = 220; 36 % Aβ+) and patients with Mild Cognitive Impairment (MCI) (N = 143; 51 % Aβ+) were utilized. Functional redundancy was calculated from resting state fMRI data using a graph theoretical approach by summing the direct and indirect paths (path lengths = 1–4) between each region of interest and its 263 functional connections. Posterior HC, but not anterior HC or LFC, redundancy was significantly lower in Aβ+ than Aβ-groups, regardless of diagnosis. Posterior HC redundancy related to higher education and better episodic memory, but it did not moderate the Aβ-cognition relationships across the diagnostic groups. Together, these findings suggest that posterior HC redundancy captures network disruption that parallels selective vulnerability to Aβ deposition. Further, our findings indicate that functional redundancy may underscore a network metric different from global functional connectivity of the LFC.
{"title":"Functional redundancy of the posterior hippocampi is selectively disrupted in non-demented older adults with β-amyloid deposition","authors":"Jenna K. Blujus , Michael W. Cole , Elena K. Festa , Stephen L. Buka , Stephen P. Salloway , William C. Heindel , Hwamee Oh , the Alzheimer's Disease Neuroimaging Initiative","doi":"10.1016/j.ynirp.2025.100255","DOIUrl":"10.1016/j.ynirp.2025.100255","url":null,"abstract":"<div><div>Several neural mechanisms underlying resilience to Alzheimer's disease (AD) have been proposed, including redundant neural connections between the posterior hippocampi and all other brain regions, and global functional connectivity of the left frontal cortex (LFC). Here, we investigated if functional redundancy of the hippocampus (HC) and LFC underscores neural resilience in the presence of early AD pathologies. From the ADNI database, cognitively normal older adults (CN) (N = 220; 36 % A<em>β</em>+) and patients with Mild Cognitive Impairment (MCI) (N = 143; 51 % A<em>β</em>+) were utilized. Functional redundancy was calculated from resting state fMRI data using a graph theoretical approach by summing the direct and indirect paths (path lengths = 1–4) between each region of interest and its 263 functional connections. Posterior HC, but not anterior HC or LFC, redundancy was significantly lower in A<em>β</em>+ than A<em>β</em>-groups, regardless of diagnosis. Posterior HC redundancy related to higher education and better episodic memory, but it did not moderate the A<em>β</em>-cognition relationships across the diagnostic groups. Together, these findings suggest that posterior HC redundancy captures network disruption that parallels selective vulnerability to A<em>β</em> deposition. Further, our findings indicate that functional redundancy may underscore a network metric different from global functional connectivity of the LFC.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"5 2","pages":"Article 100255"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-03-29DOI: 10.1016/j.ynirp.2025.100258
Marta Czime Litwińczuk , Shruti Garg , Stephen R. Williams , Jonathan Green , Caroline Lea-Carnall , Nelson J. Trujillo-Barreto
Introduction
In a previous study, we examined the effect of atDCS on working memory task performance and modulation of the inhibitory neurotransmitter, gamma-aminobutyric acid (GABA), in the dorsolateral prefrontal cortex (dlPFC). The present study investigates whether tDCS modulates effective connectivity during the task, specifically assessing whether tDCS alters interactions between neuronal populations.
Methods
Eighteen adolescents with Neurofibromatosis Type 1 (NF1) completed a single-blind sham-controlled cross-over randomised tDCS trial (with the anode at F3 and cathode at Cz). Dynamic causal modelling was used to estimate the effective connectivity between regions that showed working memory effects from the fMRI. Group-level inferences for between sessions (pre- and post-stimulation) and stimulation type (tDCS and sham) effects were carried out using the parametric empirical Bayes approach. A correlation analysis was performed to relate the estimated effective connectivity parameters of left dlPFC pre-tDCS and post-tDCS to the concentration of GABA measured via magnetic resonance spectroscopy (MRS-GABA). Next, correlation analysis was repeated using all working memory performance and all pre-tDCS and post-tDCS connectivity parameters.
Results
It was found that tDCS decreased average excitatory connectivity from dlPFC to left superior frontal gyrus and increased average excitatory connectivity to left globus pallidus. Further, reduced average intrinsic (inhibitory) connectivity of left dlPFC was associated with lower MRS-GABA. However, none of the connectivity parameters of dlPFC showed any association with performance on a working memory task.
Conclusions
These findings suggest that tDCS reorganised connectivity from frontal to fronto-striatal connectivity. As tDCS-related changes were not specific to the effect of working memory, they may have impacted general cognitive control processes. In addition, by reducing MRS-GABA, tDCS might make dlPFC more sensitive and responsive to external stimulation, such as performance of cognitive tasks.
{"title":"Non-invasive brain stimulation reorganises effective connectivity during a working memory task in individuals with Neurofibromatosis Type 1","authors":"Marta Czime Litwińczuk , Shruti Garg , Stephen R. Williams , Jonathan Green , Caroline Lea-Carnall , Nelson J. Trujillo-Barreto","doi":"10.1016/j.ynirp.2025.100258","DOIUrl":"10.1016/j.ynirp.2025.100258","url":null,"abstract":"<div><h3>Introduction</h3><div>In a previous study, we examined the effect of atDCS on working memory task performance and modulation of the inhibitory neurotransmitter, gamma-aminobutyric acid (GABA), in the dorsolateral prefrontal cortex (dlPFC). The present study investigates whether tDCS modulates effective connectivity during the task, specifically assessing whether tDCS alters interactions between neuronal populations.</div></div><div><h3>Methods</h3><div>Eighteen adolescents with Neurofibromatosis Type 1 (NF1) completed a single-blind sham-controlled cross-over randomised tDCS trial (with the anode at F3 and cathode at Cz). Dynamic causal modelling was used to estimate the effective connectivity between regions that showed working memory effects from the fMRI. Group-level inferences for between sessions (pre- and post-stimulation) and stimulation type (tDCS and sham) effects were carried out using the parametric empirical Bayes approach. A correlation analysis was performed to relate the estimated effective connectivity parameters of left dlPFC pre-tDCS and post-tDCS to the concentration of GABA measured via magnetic resonance spectroscopy (MRS-GABA). Next, correlation analysis was repeated using all working memory performance and all pre-tDCS and post-tDCS connectivity parameters.</div></div><div><h3>Results</h3><div>It was found that tDCS decreased average excitatory connectivity from dlPFC to left superior frontal gyrus and increased average excitatory connectivity to left globus pallidus. Further, reduced average intrinsic (inhibitory) connectivity of left dlPFC was associated with lower MRS-GABA. However, none of the connectivity parameters of dlPFC showed any association with performance on a working memory task.</div></div><div><h3>Conclusions</h3><div>These findings suggest that tDCS reorganised connectivity from frontal to fronto-striatal connectivity. As tDCS-related changes were not specific to the effect of working memory, they may have impacted general cognitive control processes. In addition, by reducing MRS-GABA, tDCS might make dlPFC more sensitive and responsive to external stimulation, such as performance of cognitive tasks.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"5 2","pages":"Article 100258"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-17DOI: 10.1016/j.ynirp.2025.100261
Danah Bakir, Christian Konopka, Sindhu Pisati, Syed Shah, Shashi Maryala, Andre Catalano, Faisal Ibrahim, Hesham Allam
{"title":"A case report of reversible ischemic MRI changes and discussion of possible link to kratom use","authors":"Danah Bakir, Christian Konopka, Sindhu Pisati, Syed Shah, Shashi Maryala, Andre Catalano, Faisal Ibrahim, Hesham Allam","doi":"10.1016/j.ynirp.2025.100261","DOIUrl":"10.1016/j.ynirp.2025.100261","url":null,"abstract":"","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"5 2","pages":"Article 100261"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-14DOI: 10.1016/j.ynirp.2025.100259
Johanna C. Walker , Conner Swineford , Krupali R. Patel , Lea R. Dougherty , Jillian Lee Wiggins
The recent emergence of deep learning methods, particularly convolutional neural networks (CNNs), applied to fMRI data presents a promising avenue in psychiatry research, offering advantages over traditional analyses by requiring minimal assumptions and enabling detection of higher-level patterns and intricate, nonlinear relationships within inherently complex fMRI data. Irritability, defined as a lowered threshold for angry responses to blocked rewards, is a promising neurodevelopmental marker for mental health risk due to its robust, transdiagnostic predictive power in youth. In this study, data from the Adolescent Brain and Cognitive Development (ABCD) baseline sample (N = 6065) were utilized for a novel application of a 3D CNN to whole-brain fMRI data acquired during the reward anticipation period of the monetary incentive delay task to predict parent-reported youth irritability severity, measured dimensionally. Regression activation mapping (RAM) was employed to extract feature maps of brain regions most predictive of irritability severity from the model. The model demonstrated satisfactory accuracy, with a mean squared error (MSE) of 1.82, and predicted irritability severity scores with a mean absolute error (MAE) of 0.48 ± 1.54 SD from the true scores. Notably, feature maps revealed bilateral representation of key regions implicated in emotional response and reward processing, including the caudate nucleus, amygdala, parahippocampal gyrus, and hippocampus. This study underscores the potential for 3D CNNs to predict significant, dimensional clinical outcomes such as irritability severity using fMRI data.
{"title":"Deep learning identification of reward-related neural substrates of preadolescent irritability: A novel 3D CNN application for fMRI","authors":"Johanna C. Walker , Conner Swineford , Krupali R. Patel , Lea R. Dougherty , Jillian Lee Wiggins","doi":"10.1016/j.ynirp.2025.100259","DOIUrl":"10.1016/j.ynirp.2025.100259","url":null,"abstract":"<div><div>The recent emergence of deep learning methods, particularly convolutional neural networks (CNNs), applied to fMRI data presents a promising avenue in psychiatry research, offering advantages over traditional analyses by requiring minimal assumptions and enabling detection of higher-level patterns and intricate, nonlinear relationships within inherently complex fMRI data. Irritability, defined as a lowered threshold for angry responses to blocked rewards, is a promising neurodevelopmental marker for mental health risk due to its robust, transdiagnostic predictive power in youth. In this study, data from the Adolescent Brain and Cognitive Development (ABCD) baseline sample (<em>N</em> = 6065) were utilized for a novel application of a 3D CNN to whole-brain fMRI data acquired during the reward anticipation period of the monetary incentive delay task to predict parent-reported youth irritability severity, measured dimensionally. Regression activation mapping (RAM) was employed to extract feature maps of brain regions most predictive of irritability severity from the model. The model demonstrated satisfactory accuracy, with a mean squared error (MSE) of 1.82, and predicted irritability severity scores with a mean absolute error (MAE) of 0.48 ± 1.54 SD from the true scores. Notably, feature maps revealed bilateral representation of key regions implicated in emotional response and reward processing, including the caudate nucleus, amygdala, parahippocampal gyrus, and hippocampus. This study underscores the potential for 3D CNNs to predict significant, dimensional clinical outcomes such as irritability severity using fMRI data.</div></div>","PeriodicalId":74277,"journal":{"name":"Neuroimage. Reports","volume":"5 2","pages":"Article 100259"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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