Cognitive impairment after focal brain lesions is better predicted by damage to structural than functional network hubs

Disrupted structural network resilience in atherosclerosis: A large-scale cohort study

BACKGROUND

Atherosclerosis is a major factor in cognitive decline among aging individuals and is frequently linked to the accumulation of white matter hyperintensities. Brain resilience, which represents the brain's capacity to withstand external disruptions, remains poorly understood in terms of how atherosclerosis impacts it and, in turn, influences cognition. Here, we investigated the relationship between atherosclerosis, white matter hyperintensities, and structural network resilience, along with their combined effects on cognitive performance.

METHODS

We utilized data from the large-scale community cohort Polyvascular Evaluation for Cognitive Impairment and Vascular Events (n = 2160). Whole-brain structural connections were constructed, and structural disconnections were simulated based on white matter hyperintensities. SNR, serving as a marker to quantify structural network resilience, is defined by the similarity of hub nodes between the original network and its disconnected counterpart.

RESULTS

SNR showed higher odds ratios compared to white matter hyperintensities in relation to arterial status. Additionally, chain mediation analysis indicated that cognitive decline associated with atherosclerosis was partially mediated by both white matter hyperintensities and structural network resilience. Atherosclerosis accelerates the degradation of brain structural network resilience as age increases.

CONCLUSIONS

These findings suggest that SNR could offer complementary insights into cognitive decline caused by atherosclerosis and serve as a potential biomarker of brain health in atherosclerotic conditions. Additionally, SNR may act as an indicator for guiding the selection of future therapies for atherosclerosis.

Experimental Ischemic Stroke Induces Secondary Bihemispheric White Matter Degeneration and Long-Term Cognitive Impairment

Clinical studies have identified widespread white matter degeneration in ischemic stroke patients. However, contemporary research in stroke has predominately focused on the infarct and periinfarct penumbra regions. The involvement of white matter degeneration after ischemic stroke and its contribution to post-stroke cognitive impairment and dementia (PSCID) has remained less explored in experimental models. In this study, we examined the progression of locomotor and cognitive function up to 4 months after inducing ischemic stroke by middle cerebral artery occlusion in young adult rats. Despite evident ongoing locomotor recovery, long-term cognitive and affective impairments persisted after ischemic stroke, as indicated by Morris water maze, elevated plus maze, and open field performance. At 4 months after stroke, multimodal MRI was conducted to assess white matter degeneration. T2-weighted MRI (T2WI) unveiled bilateral cerebroventricular enlargement after ischemic stroke. Fluid Attenuated Inversion Recovery MRI (FLAIR) revealed white matter hyperintensities in the corpus callosum and fornix across bilateral hemispheres. A positive association between the volume of white matter hyperintensities and total cerebroventricular volume was noted in stroke rats. Further evidence of bilateral white matter degeneration was indicated by the reduction of fractional anisotropy and quantitative anisotropy at bilateral corpus callosum in diffusion-weighted MRI (DWI) analysis. Additionally, microglia and astrocyte activation were identified in the bilateral corpus callosum after stroke. Our study suggests that experimental ischemic stroke induced by MCAO in young rat replicate long-term cognitive impairment and bihemispheric white matter degeneration observed in ischemic stroke patients. This model provides an invaluable tool for unraveling the mechanisms underlying post-stroke secondary white matter degeneration and its contribution to PSCID.

A Clinical Neuroimaging Platform for Rapid, Automated Lesion Detection and Personalized Post-Stroke Outcome Prediction

Predicting long-term functional outcomes for individuals with stroke is a significant challenge. Solving this challenge will open new opportunities for improving stroke management by informing acute interventions and guiding personalized rehabilitation strategies. The location of the stroke is a key predictor of outcomes, yet no clinically deployed tools incorporate lesion location information for outcome prognostication. This study responds to this critical need by introducing a fully automated, three-stage neuroimaging processing and machine learning pipeline that predicts personalized outcomes from clinical imaging in adult ischemic stroke patients. In the first stage, our system automatically processes raw DICOM inputs, registers the brain to a standard template, and uses deep learning models to segment the stroke lesion. In the second stage, lesion location and automatically derived network features are input into statistical models trained to predict long-term impairments from a large independent cohort of lesion patients. In the third stage, a structured PDF report is generated using a large language model that describes the stroke's location, the arterial distribution, and personalized prognostic information. We demonstrate the viability of this approach in a proof-of-concept application predicting select cognitive outcomes in a stroke cohort. Brain-behavior models were pre-trained to predict chronic impairment on 28 different cognitive outcomes in a large cohort of patients with focal brain lesions (N=604). The automated pipeline used these models to predict outcomes from clinically acquired MRIs in an independent ischemic stroke cohort (N=153). Starting from raw clinical DICOM images, we show that our pipeline can generate outcome predictions for individual patients in less than 3 minutes with 96% concordance relative to methods requiring manual processing. We also show that prediction accuracy is enhanced using models that incorporate lesion location, lesion-associated network information, and demographics. Our results provide a strong proof-of-concept and lay the groundwork for developing imaging-based clinical tools for stroke outcome prognostication.

A deep-learning model for predicting post-stroke cognitive impairment based on brain network damage

Background

Post-stroke cognitive impairment (PSCI) is a common and severe complication following acute lacunar stroke (ALS). Due to the limitations of current assessment tools and imaging methods, the early diagnosis of PSCI within 3 months of ALS remaining challenging. This study aimed to develop an effective, reliable, and accurate deep-learning method to predict PSCI within 3 months of ALS.

Methods

In total, 100 ALS patients were enrolled in the study, of whom 39 were diagnosed with PSCI and 61 were non-PSCI. First, we quantified three-dimensional (3D) gray-matter damage and white-matter tract disconnection, providing both regional damage (RD) and structural disconnection (SDC) higher-dimensional insights into brain network disruption. Second, we developed a novel deep-learning model based on ResNet18, integrating 3D RD, SDC, and diffusion-weighted imaging (DWI) to provide a comprehensive analysis of brain network integrity and predict PSCI. Finally, we compared the performance of our method with other methods, and visualized brain network damage associated with PSCI.

Results

Our model showed strong predictive performance and had a mean accuracy (ACC) of 0.820±0.024, an area under the curve (AUC) of 0.795±0.068, a sensitivity (SEN) of 0.746±0.121, a specificity (SPE) of 0.869±0.044, and a F1-score (F1) of 0.760±0.050 in the five-fold cross-validation, outperforming existing models. In the PSCI patients, brain network damage significantly affected the salience, default mode, and somatic motor networks.

Conclusions

This study not only established a model that can accurately predict PSCI, it also identified potential targets for symptom-based treatments, offering new insights into PSCI.

Dementia-related adverse events associated with direct oral anticoagulants use: a real-world, pharmacovigilance study based on the FAERS database

BACKGROUND

Direct oral anticoagulants (DOACs) are commonly used to prevent and treat thromboembolic diseases. This study aimed to assess and compare dementia related adverse events (AEs) associated with DOACs.

RESEARCH DESIGN AND METHODS

AEs related to DOACs from January 2014 to June 2023 were extracted from the FDA Adverse Event Reporting System (FAERS) database. Disproportionality analysis methods, including reporting odds ratio (ROR), proportional reporting ratio, Bayesian Confidence Propagation Neural Network, and Multi-Item Gamma Poisson Shrinker, were used to evaluate the association between DOACs and dementia-related AEs.

RESULTS

There were 12,692,968 AEs reported in FAERS after deduplication. Among these, 165, 206, 1574, and 12 dementia-related AEs that were attributed to dabigatran, rivaroxaban, apixaban, and edoxaban, respectively. Apixaban showed the strongest association with dementia-related AEs (ROR 7.66, 95% confidence interval (CI) 7.27-8.06), while rivaroxaban had the lowest ROR (0.95, 95%CI 0.83-1.09). Women exhibited higher RORs for all DOACs, with apixaban showing the most significant correlation. Subgroup analysis indicated a significant link between apixaban and dementia, dementia Alzheimer's type and senile dementia.

CONCLUSIONS

Apixaban appears most associated with dementia-related AEs among DOACs, whereas rivaroxaban poses a lower risk. Further research is needed to validate these findings through large-scale prospective studies.

Diffusion Tensor Imaging 3D Tractography-Guided, Individualized, Transsulcul Approach for Subcortical Hematoma Evacuation Using BrainPath/Myriad

sICH (spontaneous intracerebral hemorrhage) is a major cause of death and disability. Traditional surgical evacuation, while beneficial, risks damaging healthy tissue. Minimally invasive surgery (MIS) offers a promising alternative. This study explores the feasibility and safety of diffusion tensor imaging (DTI)-guided, trans-sulcal MIS with BrainPath/Myriad NICO Corporation (Indianapolis, IN, USA) for sICH evacuation. DTI/tractography (DTT) visualizes critical pathways like the corticospinal tract (CST), aiding surgical planning and potentially predicting motor function recovery post-surgery. Our objectives include i) assessing the feasibility and safety of DTT-guided, trans-sulcal MIS with BrainPath/Myriad for sICH evacuation. ii) Evaluating DTT's utility in surgical planning and its potential role in predicting motor function recovery. Three sICH patients underwent pre-operative DTT with CST involvement graded A (direct injury) to E (displacement). Based on DTT, surgical trajectories using three trans-sulcal approaches were planned to avoid the CST. MIS with BrainPath/Myriad was performed aiming for <15 mL residual hematoma. Post-operative DTT and motor function follow-up (≥3 months) were conducted. Three patients completed pre- and post-operative DTT scans. All were middle-aged males with sympathomimetic abuse history. Two had Type A CST involvement, and one had Type D. Both Type A patients recovered well but showed no significant motor improvement. The Type D patient showed motor improvement. All patients completed the three-month follow-up. Our limited data suggests that DTI-guided, trans-sulcal MIS with BrainPath/Myriad for sICH evacuation is feasible and safe. DTT seems valuable for surgical planning and potentially predicts motor function recovery. Further studies with more patients are needed to confirm these findings.

White matter correlates of dissociation in a diverse sample of trauma-exposed women

Dissociation is a common response to trauma linked to functional brain disruptions in brain networks subserving emotion regulation and multisensory integration; however, structural neural correlates of dissociation are less known, particularly abnormalities in stress-sensitive white matter (WM) tracts. The present study examined associations between dissociation and WM microstructure, assessed via fractional anisotropy (FA), in a large, diverse sample of women recruited as part of a long-standing trauma study, the Grady Trauma Project (GTP). As part of GTP, 135 trauma-exposed women (18-62 years old, M=34.25, SD=12.96, 84% self-identifying as Black) were recruited, received diffusion tensor imaging, and completed the Multiscale Dissociation Inventory (MDI); FA values were extracted from ten major WM tracts of interest. Partial correlations were conducted to examine associations between dissociation facets (MDI total and subscales) and FA while covarying age and temporal signal-to-noise ratio; false discovery rate corrected p < 0.05 indicated statistical significance. FA in seven tracts showed significant negative associations with overall dissociation (MDI total score; rs<-0.19, pFDR<0.05); the corona radiata, corpus callosum, superior longitudinal fasciculus, thalamic radiation, anterior cingulum, fornix, and uncinate fasciculus. Among facets of dissociation, FA was most consistently associated with dissociative memory disturbance, showing a significant and negative association with all but one of tract of interest, (rs<-0.23, pFDR<0.05). Our findings indicated that dissociation severity was linked to proportionally lesser WM microstructural integrity in tracts involved with sensory integration, emotion regulation, memory, and self-referential processing. Disruptions in these pathways may underlie dissociative phenomena, representing important psychotherapeutic and neuromodulatory targets.

Trade-offs among brain structural network characteristics across the cognitive decline process in cerebral small vessel disease

Objectives

To investigate the potential trade-offs among brain structural network characteristics across different stages of cognitive impairment in cerebral small vessel disease (CSVD) based on diffusion tensor imaging (DTI).

Methods

A total of 264 CSVD patients, including 95 patients with non-cognitive impairment (NCI), 142 with mild cognitive impairment (MCI), 27 with vascular dementia (VaD), and 30 healthy controls (HC) underwent cognitive test and brain diffusion magnetic resonance imaging (MRI). The brain structural network was constructed using connections between 90 cortical and subcortical regions. Network characteristics, including sparsity, redundancy, global efficiency (Eg), and local efficiency (Eloc), were calculated.

Results

Sparsity and redundancy significantly declined in the NCI group compared to the HC group. Eg was significantly reduced in the MCI group compared to the NCI group. All network characteristics declined in the VaD group compared to the MCI group. In the NCI group, both sparsity and redundancy were significantly positively correlated with Montreal Cognitive Assessment (MoCA). In the MCI group, there was significant positive correlation between Eg and MoCA. In the VaD group, there was significant negative correlation between Eloc and MoCA. When controlling for sparsity, Eloc exhibited a significant negative correlation with Eg in all three CSVD groups, while redundancy displayed a significant negative correlation with Eg specifically in MCI group.

Conclusion

Our study provides evidence for the heterogeneous alterations in brain structural network across different stages of cognitive impairment in CSVD. The disconnection of brain structural network at NCI stage is mainly the loss of redundant connections. The decline of Eg is the vital factor for cognitive impairment at MCI stage. The decline of all network characteristics is the prominent manifestation at VaD stage. Throughout the cognitive decline process in CSVD, there are trade-offs among the brain network wiring cost, integration, and segregation.

What traditional neuropsychological assessment got wrong about mild traumatic brain injury. II: limitations in test development, research design, statistical and psychometric issues

PRIMARY OBJECTIVE

This is Part II of a four-part opinion review on traditional neuropsychological assessment methods and findings associated with mild traumatic brain injury (mTBI). This Part II review focuses on historical, psychometric and statistical issues involving traditional neuropsychological methods that have been used in neuropsychological outcome studies of mTBI, but demonstrates the critical limitations of traditional methods.

RESEARCH DESIGN

This is an opinion review.

METHODS AND PROCEDURES

Traditional neuropsychological tests are dated and lack specificity in evaluating such a heterogenous and complex injury as occurs with mTBI.

MAIN OUTCOME AND RESULTS

In this review, we demonstrate traditional neuropsychological methods were never developed as standalone measures for detecting subtle changes in neurocognitive or neurobehavioral functioning and likewise, never designed to address the multifaceted issues related to underlying mTBI neuropathology symptom burden from having sustained a concussive brain injury.

CONCLUSIONS

For neuropsychological assessment to continue to contribute to clinical practice and outcome literature involving mTBI, major innovative changes are needed that will likely require technological advances of novel assessment techniques more specifically directed to evaluating the mTBI patient. These will be discussed in Part IV.

Poststroke seizures and epilepsy increase the risk of dementia among stroke survivors: A population-based study

OBJECTIVE

With global aging, the occurrence of stroke and associated outcomes like dementia are on the rise. Seizures and epilepsy are common poststroke complications and have a strong connection to subsequent dementia. This study examines the relationship between poststroke seizures (PSS) or poststroke epilepsy (PSE) and dementia using a national health care database.

METHODS

We conducted a retrospective study using data from the Taiwan National Health Insurance Research Database from 2009 to 2020. We identified acute stroke patients from 2010 to 2015, excluding those with pre-existing neurological conditions. Based on age, sex, stroke severity level, and the year of index stroke, patients with PSS or PSE were matched to those without. The main outcome was incident dementia.

RESULTS

This study included 62 968 patients with an average age of 63 years, with males accounting for 62.9%. Of them, 60.3% had ischemic strokes, and 39.7% had hemorrhagic strokes. After an average follow-up period of 5.2 years, dementia developed in 15.9% of patients who had PSS or PSE, as opposed to 8.4% of those without these conditions. A time-dependent Fine and Gray competing risk analysis showed that PSS and PSE were significantly associated with dementia across all stroke types. Subgroup analyses revealed significantly increased risk of dementia across all age groups (<50, 50-64, and ≥65 years), sexes, and various stroke severity levels. The link between PSS or PSE and dementia was particularly pronounced in men, with a less distinct correlation in women.

SIGNIFICANCE

The risk of incident dementia was higher in patients with PSS or PSE. The potential for therapeutic interventions for seizures and epilepsy to reduce poststroke dementia underscores the importance of seizure screening and treatment in stroke survivors.

Edge Density Imaging Identifies White Matter Biomarkers of Late-Life Obesity and Cognition

Alzheimer disease (AD) and obesity are related to disruptions in the white matter (WM) connectome. We examined the link between the WM connectome and obesity and AD through edge-density imaging/index (EDI), a tractography-based method that characterizes the anatomical embedding of tractography connections. A total of 60 participants, 30 known to convert from normal cognition or mild-cognitive impairment to AD within a minimum of 24 months of follow up, were selected from the Alzheimer disease Neuroimaging Initiative (ADNI). Diffusion-weighted MR images from the baseline scans were used to extract fractional anisotropy (FA) and EDI maps that were subsequently averaged using deterministic WM tractography based on the Desikan-Killiany atlas. Multiple linear and logistic regression analysis were used to identify the weighted sum of tract-specific FA or EDI indices that maximized correlation to body-mass-index (BMI) or conversion to AD. Participants from the Open Access Series of Imaging Studies (OASIS) were used as an independent validation for the BMI findings. The edge-density rich, periventricular, commissural and projection fibers were among the most important WM tracts linking BMI to FA as well as to EDI. WM fibers that contributed significantly to the regression model related to BMI overlapped with those that predicted conversion; specifically in the frontopontine, corticostriatal, and optic radiation pathways. These results were replicated by testing the tract-specific coefficients found using ADNI in the OASIS-4 dataset. WM mapping with EDI enables identification of an abnormal connectome implicated in both obesity and conversion to AD.

Predicting Cognitive Functioning for Patients with a High-Grade Glioma: Evaluating Different Representations of Tumor Location in a Common Space

Cognitive functioning is increasingly considered when making treatment decisions for patients with a brain tumor in view of a personalized onco-functional balance. Ideally, one can predict cognitive functioning of individual patients to make treatment decisions considering this balance. To make accurate predictions, an informative representation of tumor location is pivotal, yet comparisons of representations are lacking. Therefore, this study compares brain atlases and principal component analysis (PCA) to represent voxel-wise tumor location. Pre-operative cognitive functioning was predicted for 246 patients with a high-grade glioma across eight cognitive tests while using different representations of voxel-wise tumor location as predictors. Voxel-wise tumor location was represented using 13 different frequently-used population average atlases, 13 randomly generated atlases, and 13 representations based on PCA. ElasticNet predictions were compared between representations and against a model solely using tumor volume. Preoperative cognitive functioning could only partly be predicted from tumor location. Performances of different representations were largely similar. Population average atlases did not result in better predictions compared to random atlases. PCA-based representation did not clearly outperform other representations, although summary metrics indicated that PCA-based representations performed somewhat better in our sample. Representations with more regions or components resulted in less accurate predictions. Population average atlases possibly cannot distinguish between functionally distinct areas when applied to patients with a glioma. This stresses the need to develop and validate methods for individual parcellations in the presence of lesions. Future studies may test if the observed small advantage of PCA-based representations generalizes to other data.

Indirect functional connectivity does not predict overall survival in glioblastoma

BACKGROUND

Lesion network mapping (LNM) is a popular framework to assess clinical syndromes following brain injury. The classical approach involves embedding lesions from patients into a normative functional connectome and using the corresponding functional maps as proxies for disconnections. However, previous studies indicated limited predictive power of this approach in behavioral deficits. We hypothesized similarly low predictiveness for overall survival (OS) in glioblastoma (GBM).

METHODS

A retrospective dataset of patients with GBM was included (n = 99). Lesion masks were registered in the normative space to compute disconnectivity maps. The brain functional normative connectome consisted in data from 173 healthy subjects obtained from the Human Connectome Project. A modified version of the LNM was then applied to core regions of GBM masks. Linear regression, classification, and principal component (PCA) analyses were conducted to explore the relationship between disconnectivity and OS. OS was considered both as continuous and categorical (low, intermediate, and high survival) variable.

RESULTS

The results revealed no significant associations between OS and network disconnection strength when analyzed at both voxel-wise and classification levels. Moreover, patients stratified into different OS groups did not exhibit significant differences in network connectivity patterns. The spatial similarity among the first PCA of network maps for each OS group suggested a lack of distinctive network patterns associated with survival duration.

CONCLUSIONS

Compared with indirect structural measures, functional indirect mapping does not provide significant predictive power for OS in patients with GBM. These findings are consistent with previous research that demonstrated the limitations of indirect functional measures in predicting clinical outcomes, underscoring the need for more comprehensive methodologies and a deeper understanding of the factors influencing clinical outcomes in this challenging disease.

Discovering the gene-brain-behavior link in autism via generative machine learning

Autism is traditionally diagnosed behaviorally but has a strong genetic basis. A genetics-first approach could transform understanding and treatment of autism. However, isolating the gene-brain-behavior relationship from confounding sources of variability is a challenge. We demonstrate a novel technique, 3D transport-based morphometry (TBM), to extract the structural brain changes linked to genetic copy number variation (CNV) at the 16p11.2 region. We identified two distinct endophenotypes. In data from the Simons Variation in Individuals Project, detection of these endophenotypes enabled 89 to 95% test accuracy in predicting 16p11.2 CNV from brain images alone. Then, TBM enabled direct visualization of the endophenotypes driving accurate prediction, revealing dose-dependent brain changes among deletion and duplication carriers. These endophenotypes are sensitive to articulation disorders and explain a portion of the intelligence quotient variability. Genetic stratification combined with TBM could reveal new brain endophenotypes in many neurodevelopmental disorders, accelerating precision medicine, and understanding of human neurodiversity.

Influence of Infarct Morphology and Patterns on Cognitive Outcomes After Endovascular Thrombectomy

BACKGROUND

To assess the association of qualitative and quantitative infarct characteristics and 3 cognitive outcome tests, namely the Montreal Cognitive Assessment (MOCA) for mild cognitive impairment, the Boston Naming Test for visual confrontation naming, and the Sunnybrook Neglect Assessment Procedure for neglect, in large vessel occlusion stroke.

METHODS

Secondary observational cohort study using data from the randomized-controlled ESCAPE-NA1 trial (Safety and Efficacy of Nerinetide in Subjects Undergoing Endovascular Thrombectomy for Stroke), in which patients with large vessel occlusion undergoing endovascular treatment were randomized to receive either intravenous Nerinetide or placebo. MOCA, Sunnybrook Neglect Assessment Procedure, and 15-item Boston Naming Test were obtained at 90 days. Total infarct volume, gray matter, and white matter infarct volumes were manually measured on 24-hour follow-up imaging. Infarcts were also visually classified as either involving the gray matter only or both the gray and white matter and scattered versus territorial. Associations of infarct variables and cognitive outcomes were analyzed using multivariable ordinal or binary logistic regression models.

RESULTS

Of 1105 patients enrolled in ESCAPE-NA1, 1026 patients with visible infarcts on 24-hour follow-up imaging were included. MOCA and Sunnybrook Neglect Assessment Procedure were available for 706 (68.8%) patients and the 15-item Boston Naming Test was available for 682 (66.5%) patients. Total infarct volume was associated with worse MOCA scores (adjusted common odds ratio per 10 mL increase, 1.05 [95% CI, 1.04-1.06]). After adjusting for baseline variables and total infarct volume, mixed gray and white matter involvement (versus gray matter-only adjusted common odds ratio, 1.92 [95% CI, 1.37-2.69]), white matter infarct volume (adjusted common odds ratio per 10 mL increase 1.36 [95% CI, 1.18-1.58]) and territorial (versus scattered) infarct pattern (adjusted common odds ratio, 1.65 [95% CI, 1.15-2.38]) were associated with worse MOCA scores. Results for Sunnybrook Neglect Assessment Procedure and 15-item Boston Naming Test were similar, except for the territorial infarct pattern, which did not reach statistical significance in multivariable analysis.

CONCLUSIONS

Besides total infarct volume, infarcts that involve the white matter and that show a territorial distribution were associated with worse cognitive outcomes, even after adjusting for total infarct volume.

MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline

Large-scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that requires many software packages with complex dependencies and high computational costs. We developed MaPPeRTrac, an edge-centric tractography pipeline that simplifies and accelerates this process in a wide range of high-performance computing (HPC) environments. It fully automates either probabilistic or deterministic tractography, starting from a subject's magnetic resonance imaging (MRI) data, including structural and diffusion MRI images, to the edge density image (EDI) of their structural connectomes. Dependencies are containerized with Singularity (now called Apptainer) and decoupled from code to enable rapid prototyping and modification. Data derivatives are organized with the Brain Imaging Data Structure (BIDS) to ensure that they are findable, accessible, interoperable, and reusable following FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming framework, resulting in the creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so it can accelerate brain connectome research for a broader user community. MaPPeRTrac is available at: https://github.com/LLNL/mappertrac .

Risk factors for post-cerebral infarction cognitive dysfunction in older adults: a retrospective study

OBJECTIVE

Our research aims to elucidate the significance of type 2 diabetes (T2D) and provides an insight into a novel risk model for post-cerebral infarction cognitive dysfunction (PCICD).

METHODS

Our study recruited inpatients hospitalized with cerebral infarction in Xijing hospital, who underwent cognitive assessment of Mini-Mental State Examination (MMSE) from January 2010 to December 2021. Cognitive status was dichotomized into normal cognition and cognitive impairment. Collected data referred to Demographic Features, Clinical Diseases, scale tests, fluid biomarkers involving inflammation, coagulation function, hepatorenal function, lipid and glycemic management.

RESULTS

In our pooled dataset from 924 eligible patients, we included 353 in the final analysis (age range 65-91; 30.31% female). Multivariate logistic regression analysis was performed to show that Rural Areas (OR = 1.976, 95%CI = 1.111-3.515, P = 0.020), T2D (OR = 2.125, 95%CI = 1.267-3.563, P = 0.004), Direct Bilirubin (OR = 0.388, 95%CI = 0.196-0.769, P = 0.007), Severity of Dependence in terms of Barthel Index (OR = 1.708, 95%CI = 1.193-2.445, P = 0.003) that were independently associated with PCICD, constituting a model with optimal predictive efficiency.

CONCLUSION

To the best of our knowledge, this study provides a practicable map of strategical predictors to robustly identify cognitive dysfunction at risk of post-cerebral infarction for clinicians in a broad sense. Of note, our findings support that the decline in serum direct bilirubin (DBil) concentration is linked to protecting cognitive function.

Association between functional network connectivity, retina structure and microvasculature, and visual performance in patients after thalamic stroke: An exploratory multi-modality study

BACKGROUND AND OBJECTIVE

Neuro-ophthalmologic symptoms and retinal changes have been increasingly observed following thalamic stroke, and there is mounting evidence indicating distinct alterations occurring in the vision-related functional network. However, the intrinsic correlations between these changes are not yet fully understood. Our objective was to explore the altered patterns of functional network connectivity and retina parameters, and their correlations with visual performance in patients with thalamic stroke.

METHODS

We utilized resting-state functional MRI to obtain multi-modular functional connectivity (FC), and optical coherence tomography-angiography to measure various retina parameters, such as the retinal nerve fiber layer (RNFL), ganglion cell-inner plexiform layer (GCIPL), superficial vascular complex (SVC), and deep vascular complex. Visual acuity (VA) was used as a metric for visual performance.

RESULTS

We included 46 patients with first-ever unilateral thalamic stroke (mean age 59.74 ± 10.02 years, 33 males). Significant associations were found between FC of attention-to-default mode and SVC, RNFL, and GCIPL, as well as between FC of attention-to-visual and RNFL (p < .05). Both RNFL and GCIPL exhibited significant associations with FC of visual-to-visual (p < .05). Only GCIPL showed an association with VA (p = .038). Stratified analysis based on a disease duration of 6 months revealed distinct and significant linking patterns in multi-modular FC and specific retina parameters, with varying correlations with VA in each subgroup.

CONCLUSION

These findings provide valuable insight into the neural basis of the associations between brain network dysfunction and impaired visual performance in patients with thalamic stroke. Our novel findings have the potential to inform future targeted and individualized therapies. However, further comprehensive studies are necessary to validate our results.

Incident Dementia After Spontaneous Intracerebral Hemorrhage

 Post-stroke cognitive impairment and dementia (PSCID) is a complication that affects long-term functional outcomes after stroke. Studies on dementia after long-term follow-up in stroke have focused predominantly on ischemic stroke, which may be different from the development of dementia after spontaneous intracerebral hemorrhage (ICH). In this review, we summarize the existing data and hypotheses on the development of dementia after spontaneous ICH, review the management of post-ICH dementia, and suggest areas for future research. Dementia after spontaneous ICH has a cumulative incidence of up to 32.0-37.4% at 5 years post-ICH. Although the pathophysiology of post-ICH dementia has not been fully understood, two main theoretical frameworks can be considered: 1) the triggering role of ICH (both primary and secondary brain injury) in precipitating cognitive decline and dementia; and 2) the contributory role of pre-existing brain pathology (including small vessel disease and neurodegenerative pathology), reduced cognitive reserve, and genetic factors predisposing to cognitive dysfunction. These pathophysiological pathways may have synergistic effects that converge on dysfunction of the neurovascular unit and disruptions in functional connectivity leading to dementia post-ICH. Management of post-ICH dementia may include screening and monitoring, cognitive therapy, and pharmacotherapy. Non-invasive brain stimulation is an emerging therapeutic modality under investigation for safety and efficacy. Our review highlights that there remains a paucity of data and standardized reporting on incident dementia after spontaneous ICH. Further research is imperative for determining the incidence, risk factors, and pathophysiology of post-ICH dementia, in order to identify new therapies for the treatment of this debilitating condition.

Neuronal tau pathology worsens late-phase white matter degeneration after traumatic brain injury in transgenic mice

Traumatic brain injury (TBI) causes diffuse axonal injury which can produce chronic white matter pathology and subsequent post-traumatic neurodegeneration with poor patient outcomes. Tau modulates axon cytoskeletal functions and undergoes phosphorylation and mis-localization in neurodegenerative disorders. The effects of tau pathology on neurodegeneration after TBI are unclear. We used mice with neuronal expression of human mutant tau to examine effects of pathological tau on white matter pathology after TBI. Adult male and female hTau.P301S (Tg2541) transgenic and wild-type (Wt) mice received either moderate single TBI (s-TBI) or repetitive mild TBI (r-mTBI; once daily × 5), or sham procedures. Acutely, s-TBI produced more extensive axon damage in the corpus callosum (CC) as compared to r-mTBI. After s-TBI, significant CC thinning was present at 6 weeks and 4 months post-injury in Wt and transgenic mice, with homozygous tau expression producing additional pathology of late demyelination. In contrast, r-mTBI did not produce significant CC thinning except at the chronic time point of 4 months in homozygous mice, which exhibited significant CC atrophy (- 29.7%) with increased microgliosis. Serum neurofilament light quantification detected traumatic axonal injury at 1 day post-TBI in Wt and homozygous mice. At 4 months, high tau and neurofilament in homozygous mice implicated tau in chronic axon pathology. These findings did not have sex differences detected. Conclusions: Neuronal tau pathology differentially exacerbated CC pathology based on injury severity and chronicity. Ongoing CC atrophy from s-TBI became accompanied by late demyelination. Pathological tau significantly worsened CC atrophy during the chronic phase after r-mTBI.

Probing bundle-wise abnormalities in patients infected with human immunodeficiency virus using fixel-based analysis: new insights into neurocognitive impairments

BACKGROUND

Changes in white matter (WM) underlie the neurocognitive damages induced by a human immunodeficiency virus (HIV) infection. This study aimed to examine using a bundle-associated fixel-based analysis (FBA) pipeline for investigating the microstructural and macrostructural alterations in the WM of the brain of HIV patients.

METHODS

This study collected 93 HIV infected patients and 45 age/education/handedness matched healthy controls (HCs) at the Beijing Youan Hospital between January 1, 2016 and December 30, 2016.All HIV patients underwent neurocognitive evaluation and laboratory testing followed by magnetic resonance imaging (MRI) scanning. In order to detect the bundle-wise WM abnormalities accurately, a specific WM bundle template with 56 tracts of interest was firstly generated by an automated fiber clustering method using a subset of subjects. Fixel-based analysis was used to investigate bundle-wise differences between HIV patients and HCs in three perspectives: fiber density (FD), fiber cross-section (FC), and fiber density and cross-section (FDC). The between-group differences were detected by a two-sample t -test with the false discovery rate (FDR) correction ( P <0.05). Furthermore, the covarying relationship in FD, FC and FDC between any pair of bundles was also accessed by the constructed covariance networks, which was subsequently compared between HIV and HCs via permutation t -tests. The correlations between abnormal WM metrics and the cognitive functions of HIV patients were explored via partial correlation analysis after controlling age and gender.

RESULTS

Among FD, FC and FDC, FD was the only metric that showed significant bundle-wise alterations in HIV patients compared to HCs. Increased FD values were observed in the bilateral fronto pontine tract, corona radiata frontal, left arcuate fasciculus, left corona radiata parietal, left superior longitudinal fasciculus III, and right superficial frontal parietal (SFP) (all FDR P <0.05). In bundle-wise covariance network, HIV patients displayed decreased FD and increased FC covarying patterns in comparison to HC ( P <0.05) , especially between associated pathways. Finally, the FCs of several tracts exhibited a significant correlation with language and attention-related functions.

CONCLUSIONS

Our study demonstrated the utility of FBA on detecting the WM alterations related to HIV infection. The bundle-wise FBA method provides a new perspective for investigating HIV-induced microstructural and macrostructural WM-related changes, which may help to understand cognitive dysfunction in HIV patients thoroughly.

Reliability and validity of DTI-based indirect disconnection measures

White matter connections enable the interaction within and between brain networks. Brain lesions can cause structural disconnections that disrupt networks and thereby cognitive functions supported by them. In recent years, novel methods have been developed to quantify the extent of structural disconnection after focal lesions, using tractography data from healthy controls. These methods, however, are indirect and their reliability and validity have yet to be fully established. In this study, we present our implementation of this approach, in a tool supplemented by uncertainty metrics for the predictions overall and at voxel-level. These metrics give an indication of the reliability and are used to compare predictions with direct measures from patients' diffusion tensor imaging (DTI) data in a sample of 95 first-ever stroke patients. Results show that, except for small lesions, the tool can predict fiber loss with high reliability and compares well to direct patient DTI estimates. Clinical utility of the method was demonstrated using lesion data from a subset of patients suffering from hemianopia. Both tract-based measures outperformed lesion localization in mapping visual field defects and showed a network consistent with the known anatomy of the visual system. This study offers an important contribution to the validation of structural disconnection mapping. We show that indirect measures of structural disconnection can be a reliable and valid substitute for direct estimations of fiber loss after focal lesions. Moreover, based on these results, we argue that indirect structural disconnection measures may even be preferable to lower-quality single subject diffusion MRI when based on high-quality healthy control datasets.

Aerobic exercise reverses aging-induced depth-dependent decline in cerebral microcirculation

Aging is a major risk factor for cognitive impairment. Aerobic exercise benefits brain function and may promote cognitive health in older adults. However, underlying biological mechanisms across cerebral gray and white matter are poorly understood. Selective vulnerability of the white matter to small vessel disease and a link between white matter health and cognitive function suggests a potential role for responses in deep cerebral microcirculation. Here, we tested whether aerobic exercise modulates cerebral microcirculatory changes induced by aging. To this end, we carried out a comprehensive quantitative examination of changes in cerebral microvascular physiology in cortical gray and subcortical white matter in mice (3-6 vs. 19-21 months old), and asked whether and how exercise may rescue age-induced deficits. In the sedentary group, aging caused a more severe decline in cerebral microvascular perfusion and oxygenation in deep (infragranular) cortical layers and subcortical white matter compared with superficial (supragranular) cortical layers. Five months of voluntary aerobic exercise partly renormalized microvascular perfusion and oxygenation in aged mice in a depth-dependent manner, and brought these spatial distributions closer to those of young adult sedentary mice. These microcirculatory effects were accompanied by an improvement in cognitive function. Our work demonstrates the selective vulnerability of the deep cortex and subcortical white matter to aging-induced decline in microcirculation, as well as the responsiveness of these regions to aerobic exercise.

The functional anatomy of dystonia: Recent developments

While dystonia has traditionally been viewed as a disorder of the basal ganglia, the involvement of other key brain structures is now accepted. However, just what these structures are remains to be defined. Neuroimaging has been an especially valuable tool in dystonia, yet traditional cross-sectional designs have not been able to separate causal from compensatory brain activity. Therefore, this chapter discusses recent studies using causal brain lesions, and animal models, to converge upon the brain regions responsible for dystonia with increasing precision. This evidence strongly implicates the basal ganglia, thalamus, brainstem, cerebellum, and somatosensory cortex, yet shows that different types of dystonia involve different nodes of this brain network. Nearly all of these nodes fall within the recently identified two-way networks connecting the basal ganglia and cerebellum, suggesting dysfunction of these specific pathways. Localisation of the functional anatomy of dystonia has strong implications for targeted treatment options, such as deep brain stimulation, and non-invasive brain stimulation.

White matter disconnection of left multiple demand network is associated with post-lesion deficits in cognitive control

Cognitive control modulates other cognitive functions to achieve internal goals and is important for adaptive behavior. Cognitive control is enabled by the neural computations distributed over cortical and subcortical areas. However, due to technical challenges in recording neural activity from the white matter, little is known about the anatomy of white matter tracts that coordinate the distributed neural computations that support cognitive control. Here, we leverage a large sample of human patients with focal brain lesions (n = 643) and investigate how lesion location and connectivity profiles account for variance in cognitive control performance. We find that lesions in white matter connecting left frontoparietal regions of the multiple demand network reliably predict deficits in cognitive control performance. These findings advance our understanding of the white matter correlates of cognitive control and provide an approach for incorporating network disconnection to predict deficits following lesions.

CarveMix: A simple data augmentation method for brain lesion segmentation

Brain lesion segmentation provides a valuable tool for clinical diagnosis and research, and convolutional neural networks (CNNs) have achieved unprecedented success in the segmentation task. Data augmentation is a widely used strategy to improve the training of CNNs. In particular, data augmentation approaches that mix pairs of annotated training images have been developed. These methods are easy to implement and have achieved promising results in various image processing tasks. However, existing data augmentation approaches based on image mixing are not designed for brain lesions and may not perform well for brain lesion segmentation. Thus, the design of this type of simple data augmentation method for brain lesion segmentation is still an open problem. In this work, we propose a simple yet effective data augmentation approach, dubbed as CarveMix, for CNN-based brain lesion segmentation. Like other mixing-based methods, CarveMix stochastically combines two existing annotated images (annotated for brain lesions only) to obtain new labeled samples. To make our method more suitable for brain lesion segmentation, CarveMix is lesion-aware, where the image combination is performed with a focus on the lesions and preserves the lesion information. Specifically, from one annotated image we carve a region of interest (ROI) according to the lesion location and geometry with a variable ROI size. The carved ROI then replaces the corresponding voxels in a second annotated image to synthesize new labeled images for network training, and additional harmonization steps are applied for heterogeneous data where the two annotated images can originate from different sources. Besides, we further propose to model the mass effect that is unique to whole brain tumor segmentation during image mixing. To evaluate the proposed method, experiments were performed on multiple publicly available or private datasets, and the results show that our method improves the accuracy of brain lesion segmentation. The code of the proposed method is available at https://github.com/ZhangxinruBIT/CarveMix.git.

The relevance of rich club regions for functional outcome post-stroke is enhanced in women

This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich-club) brain regions on functional outcome post-stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3-months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI-GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas-defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non-rich club regions. In spatial specificity analyses, we randomized the split into "rich club" and "non-rich club" regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex-specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female-specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club-combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long-term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.

Aerobic exercise reverses aging-induced depth-dependent decline in cerebral microcirculation

Aging is a major risk factor for cognitive impairment. Aerobic exercise benefits brain function and may promote cognitive health in older adults. However, underlying biological mechanisms across cerebral gray and white matter are poorly understood. Selective vulnerability of the white matter to small vessel disease and a link between white matter health and cognitive function suggests a potential role for responses in deep cerebral microcirculation. Here, we tested whether aerobic exercise modulates cerebral microcirculatory changes induced by aging. To this end, we carried out a comprehensive quantitative examination of changes in cerebral microvascular physiology in cortical gray and subcortical white matter in mice (3-6 vs. 19-21 months old), and asked whether and how exercise may rescue age-induced deficits. In the sedentary group, aging caused a more severe decline in cerebral microvascular perfusion and oxygenation in deep (infragranular) cortical layers and subcortical white matter compared with superficial (supragranular) cortical layers. Five months of voluntary aerobic exercise partly renormalized microvascular perfusion and oxygenation in aged mice in a depth-dependent manner, and brought these spatial distributions closer to those of young adult sedentary mice. These microcirculatory effects were accompanied by an improvement in cognitive function. Our work demonstrates the selective vulnerability of the deep cortex and subcortical white matter to aging-induced decline in microcirculation, as well as the responsiveness of these regions to aerobic exercise.

Therapeutic Potential and Limitation of Serotonin Type 7 Receptor Modulation

Although a number of mood-stabilising atypical antipsychotics and antidepressants modulate serotonin type 7 receptor (5-HT7), the detailed contributions of 5-HT7 function to clinical efficacy and pathophysiology have not been fully understood. The mood-stabilising antipsychotic agent, lurasidone, and the serotonin partial agonist reuptake inhibitor, vortioxetine, exhibit higher binding affinity to 5-HT7 than other conventional antipsychotics and antidepressants. To date, the initially expected rapid onset of antidepressant effects-in comparison with conventional antidepressants or mood-stabilising antipsychotics-due to 5-HT7 inhibition has not been observed with lurasidone and vortioxetine; however, several clinical studies suggest that 5-HT7 inhibition likely contributes to quality of life of patients with schizophrenia and mood disorders via the improvement of cognition. Furthermore, recent preclinical studies reported that 5-HT7 inhibition might mitigate antipsychotic-induced weight gain and metabolic complication by blocking other monoamine receptors. Further preclinical studies for the development of 5-HT7 modulation against neurodevelopmental disorders and neurodegenerative diseases have been ongoing. To date, various findings from various preclinical studies indicate the possibility that 5-HT7 modifications can provide two independent strategies. The first is that 5-HT7 inhibition ameliorates the dysfunction of inter-neuronal transmission in mature networks. The other is that activation of 5-HT7 can improve transmission dysfunction due to microstructure abnormality in the neurotransmission network-which could be unaffected by conventional therapeutic agents-via modulating intracellular signalling during the neurodevelopmental stage or via loss of neural networks with aging. This review attempts to describe the current and novel clinical applications of 5-HT7 modulation based on preclinical findings.

Thalamocortical contributions to cognitive task activity

Thalamocortical interaction is a ubiquitous functional motif in the mammalian brain. Previously (Hwang et al., 2021), we reported that lesions to network hubs in the human thalamus are associated with multi-domain behavioral impairments in language, memory, and executive functions. Here, we show how task-evoked thalamic activity is organized to support these broad cognitive abilities. We analyzed functional magnetic resonance imaging (MRI) data from human subjects that performed 127 tasks encompassing a broad range of cognitive representations. We first investigated the spatial organization of task-evoked activity and found a basis set of activity patterns evoked to support processing needs of each task. Specifically, the anterior, medial, and posterior-medial thalamus exhibit hub-like activity profiles that are suggestive of broad functional participation. These thalamic task hubs overlapped with network hubs interlinking cortical systems. To further determine the cognitive relevance of thalamic activity and thalamocortical functional connectivity, we built a data-driven thalamocortical model to test whether thalamic activity can be used to predict cortical task activity. The thalamocortical model predicted task-specific cortical activity patterns, and outperformed comparison models built on cortical, hippocampal, and striatal regions. Simulated lesions to low-dimensional, multi-task thalamic hub regions impaired task activity prediction. This simulation result was further supported by profiles of neuropsychological impairments in human patients with focal thalamic lesions. In summary, our results suggest a general organizational principle of how the human thalamocortical system supports cognitive task activity.

Mapping correlated neurological deficits after stroke to distributed brain networks

Understanding the relationships between brain organization and behavior is a central goal of neuroscience. Traditional teaching emphasizes that the human cerebrum includes many distinct areas for which damage or dysfunction would lead to a unique and specific behavioral syndrome. This teaching implies that brain areas correspond to encapsulated modules that are specialized for specific cognitive operations. However, empirically, local damage from stroke more often produces one of a small number of clusters of deficits and disrupts brain-wide connectivity in a small number of predictable ways (relative to the vast complexity of behavior and brain connectivity). Behaviors that involve shared operations show correlated deficits following a stroke, consistent with a low-dimensional behavioral space. Because of the networked organization of the brain, local damage from a stroke can result in widespread functional abnormalities, matching the low dimensionality of behavioral deficit. In alignment with this, neurological disease, psychiatric disease, and altered brain states produce behavioral changes that are highly correlated across a range of behaviors. We discuss how known structural and functional network priors in addition to graph theoretical concepts such as modularity and entropy have provided inroads to understanding this more complex relationship between brain and behavior. This model for brain disease has important implications for normal brain-behavior relationships and the treatment of neurological and psychiatric diseases.

The role of the default mode network in longitudinal functional brain reorganization of brain gliomas

The study of patients after glioma resection offers a unique opportunity to investigate brain reorganization. It is currently unknown how the whole-brain connectomic profile evolves longitudinally after surgical resection of a glioma and how this may be associated with tumor characteristics and cognitive outcome. In this longitudinal study, we investigate the impact of tumor lateralization and grade on functional connectivity (FC) in highly connected networks, or hubs, and cognitive performance. Twenty-eight patients (17 high-grade, 11 low-grade gliomas) underwent longitudinal pre/post-surgery resting-state fMRI scans and neuropsychological assessments (73 total measures). FC matrices were constructed considering as functional hubs the default mode (DMN) and fronto-parietal networks. No-hubs included primary sensory functional networks and any other no-hubs nodes. Both tumor hemisphere and grade affected brain reorganization post-resection. In right-hemisphere tumor patients, regardless of grade and relative to left-hemisphere gliomas, FC increased longitudinally after the intervention, both in terms of FC within hubs (phubs = 0.0004) and FC between hubs and no-hubs (phubs-no-hubs = 0.005). Regardless of tumor side, only lower-grade gliomas showed longitudinal FC increases relative to high-grade tumors within a precise hub network, the DMN. The neurocognitive profile was longitudinally associated with spatial features of the connectome, mainly within the DMN. We provide evidence that clinical glioma features, such as lateralization and grade, affect post-surgical longitudinal functional reorganization and cognitive recovery. The data suggest a possible role of the DMN in supporting cognition, providing useful information for prognostic prediction and surgical planning.

White matter dementia then… and now

White matter dementia (WMD) is a concept introduced in 1988 to highlight the importance of white matter pathology in producing cognitive dysfunction and dementia. Whereas gray matter, particularly the cerebral cortex, has been primarily investigated in the dementias, subcortical pathology has long been correlated with cognitive loss, and a corticocentric perspective cannot account for the full range of neurobehavioral disorders. Within the subcortical regions, white matter is prominent, accounting for about half the volume of the adult brain, and many white matter diseases, injuries, and intoxications can produce cognitive dysfunction so severe as to justify the term dementia. Recognition of this novel syndrome relied heavily on the introduction of magnetic resonance imaging (MRI) that permitted in vivo visualization of white matter lesions. Neuropsychological studies clarified the clinical presentation of WMD by identifying a profile dominated by cognitive slowing and executive dysfunction, and a precursor syndrome of mild cognitive dysfunction was proposed to identify early cognitive impairment that may later evolve to WMD. As knowledge advanced, the role of white matter in structural connectivity within distributed neural networks was elucidated. In addition, highlighting the frequent commingling of gray and white matter involvement, white matter pathology was associated with neurodegenerative diseases such as Alzheimer's disease and chronic traumatic encephalopathy, with potentially transformative clinical implications. In particular, preventive measures and treatments exploiting white matter restoration and plasticity are gaining much attention. Today, WMD has matured into a concept that not only integrates knowledge from across the spectrum of clinical neuroscience, but also informs new investigations into many perplexing disorders and enables a more complete understanding of brain-behavior relationships.

Anterior connectivity critical for recovery of connected speech after stroke

Connected speech recovers to different degrees across people after left hemisphere stroke, but white matter predictors of differential recovery from the acute stage of stroke are unknown. We assessed changes in lexical-syntactic aspects of connected speech in a longitudinal analysis of 40 individuals (18 females) from the acute stage of left hemisphere stroke (within an average of 4 days post-stroke) to subacute (within 2 months) and chronic stages (early: 6 months, late: 1 year) while measuring the extent of acute lesions on white matter tracts to identify tracts predictive of recovery. We found that acute damage to the frontal aslant tract led to a decreased recovery of the fluency and structural complexity of connected speech during the year following left hemisphere stroke. The results were independent of baseline performance, overall lesion volume and the proportion of damage to tract-adjacent grey matter. This longitudinal analysis from acute to chronic stroke provides the first evidence that recovery of fluent and structurally complex spontaneous connected speech requires intact left frontal connectivity via the frontal aslant tract. That the frontal aslant tract was critical for recovery at early as well as later stages of stroke demonstrates that anterior connectivity plays a lasting and important role for the reorganization of function related to the successful production of connected speech.

The organization of individually mapped structural and functional semantic networks in aging adults

Language function in the brain, once thought to be highly localized, is now appreciated as relying on a connected but distributed network. The semantic system is of particular interest in the language domain because of its hypothesized integration of information across multiple cortical regions. Previous work in healthy individuals has focused on group-level functional connectivity (FC) analyses of the semantic system, which may obscure interindividual differences driving variance in performance. These studies also overlook the contributions of white matter networks to semantic function. Here, we identified semantic network nodes at the individual level with a semantic decision fMRI task in 53 typically aging adults, characterized network organization using structural connectivity (SC), and quantified the segregation and integration of the network using FC. Hub regions were identified in left inferior frontal gyrus. The individualized semantic network was composed of three interacting modules: (1) default-mode module characterized by bilateral medial prefrontal and posterior cingulate regions and also including right-hemisphere homotopes of language regions; (2) left frontal module extending dorsally from inferior frontal gyrus to pre-motor area; and (3) left temporoparietal module extending from temporal pole to inferior parietal lobule. FC within Module3 and integration of the entire network related to a semantic verbal fluency task, but not a matched phonological task. These results support and extend the tri-network semantic model (Xu in Front Psychol 8: 1538 1538, 2017) and the controlled semantic cognition model (Chiou in Cortex 103: 100 116, 2018) of semantic function.

Development of quantitative and continuous measure for severity degree of Alzheimer's disease evaluated from MRI images of 761 human brains

Background

Alzheimer’s disease affects profoundly the quality of human behavior and cognition. The very broad distribution of its severity across various human subjects requires the quantitative diagnose of Alzheimer’s disease beyond the conventional tripartite classification of cohorts such as cognitively normal (CN), mild cognitive impairment (MCI), Alzheimer’s disease (AD). The unfolding of such broad distributions by the quantitative and continuous degree of AD severity is necessary for the precise diagnose in the cross-sectional study of different stages in AD.

Results

We conducted the massive reanalysis on MRI images of 761 human brains based on the accumulated bigdata of Alzheimer’s Disease Neuroimaging Initiative. The score matrix of cortical thickness profile at cortex points of subjects was constructed by statistically learning the cortical thickness data of 761 human brains. We also developed a new and simple algebraic predictor which provides the quantitative and continuous degree of AD severity of subjects along the scale from 0 for fully CN to 1 for fully AD state. The mathematical measure of a new predictor for the degree of AD severity is presented based on a covariance correlation matrix of cortical thickness profile between human subjects. One can remove the uncertainty in the determination of different stages in AD by the quantitative degree of AD severity and thus go far beyond the tripartite classification of cohorts.

Conclusions

We unfold the nature of broad distribution of AD severity of subjects even within a given cohort by the scale from 0 for fully CN to 1 for fully AD state. The quantitative and continuous degree of AD severity developed in this study would be a good practical measure for diagnosing the different stages in AD severity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04903-8.

The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks

We demonstrate a data-driven approach for calculating a "causal connectome" of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge orientations. We used this approach to construct n = 442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.25% edge density) yet were fully connected in nearly all cases. Prominent highly connected hubs of the causal connectome were situated in attentional (dorsal attention) and executive (frontoparietal and cingulo-opercular) networks. These hub networks had distinctly different connectivity profiles: attentional networks shared incoming connections with sensory regions and outgoing connections with higher cognitive networks, while executive networks primarily connected to other higher cognitive networks and had a high degree of bidirected connectivity. Virtual lesion analyses accentuated these findings, demonstrating that attentional and executive hub networks are points of critical vulnerability in the human causal connectome. These data highlight the central role of attention and executive control networks in the human cortical connectome and set the stage for future applications of data-driven causal connectivity analysis in psychiatry.

Getting Fit to Counteract Cognitive Aging: Evidence and Future Directions

Physical activity has shown tremendous promise for counteracting cognitive aging, but also tremendous variability in cognitive benefits. We describe evidence for how exercise affects cognitive and brain aging, and whether cardiorespiratory fitness is a key factor. We highlight a brain network framework as a valuable paradigm for the mechanistic insight needed to tailor physical activity for cognitive benefits.

Post-stroke outcomes predicted from multivariate lesion-behaviour and lesion network mapping

Clinicians and scientists alike have long sought to predict the course and severity of chronic post-stroke cognitive and motor outcomes, as the ability to do so would inform treatment and rehabilitation strategies. However, it remains difficult to make accurate predictions about chronic post-stroke outcomes due, in large part, to high inter-individual variability in recovery and a reliance on clinical heuristics rather than empirical methods. The neuroanatomical location of a stroke is a key variable associated with long-term outcomes, and because lesion location can be derived from routinely collected clinical neuroimaging data there is an opportunity to use this information to make empirically based predictions about post-stroke deficits. For example, lesion location can be compared to statistically weighted multivariate lesion-behaviour maps of neuroanatomical regions that, when damaged, are associated with specific deficits based on aggregated outcome data from large cohorts. Here, our goal was to evaluate whether we can leverage lesion-behaviour maps based on data from two large cohorts of individuals with focal brain lesions to make predictions of 12-month cognitive and motor outcomes in an independent sample of stroke patients. Further, we evaluated whether we could augment these predictions by estimating the structural and functional networks disrupted in association with each lesion-behaviour map through the use of structural and functional lesion network mapping, which use normative structural and functional connectivity data from neurologically healthy individuals to elucidate lesion-associated networks. We derived these brain network maps using the anatomical regions with the strongest association with impairment for each cognitive and motor outcome based on lesion-behaviour map results. These peak regional findings became the 'seeds' to generate networks, an approach that offers potentially greater precision compared to previously used single-lesion approaches. Next, in an independent sample, we quantified the overlap of each lesion location with the lesion-behaviour maps and structural and functional lesion network mapping and evaluated how much variance each could explain in 12-month behavioural outcomes using a latent growth curve statistical model. We found that each lesion-deficit mapping modality was able to predict a statistically significant amount of variance in cognitive and motor outcomes. Both structural and functional lesion network maps were able to predict variance in 12-month outcomes beyond lesion-behaviour mapping. Functional lesion network mapping performed best for the prediction of language deficits, and structural lesion network mapping performed best for the prediction of motor deficits. Altogether, these results support the notion that lesion location and lesion network mapping can be combined to improve the prediction of post-stroke deficits at 12-months.

Edge-centric analysis of stroke patients: An alternative approach for biomarkers of lesion recovery

Most neuroimaging studies of post-stroke recovery rely on analyses derived from standard node-centric functional connectivity to map the distributed effects in stroke patients. Here, given the importance of nonlocal and diffuse damage, we use an edge-centric approach to functional connectivity in order to provide an alternative description of the effects of this disorder. These techniques allow for the rendering of metrics such as normalized entropy, which describes the diversity of edge communities at each node. Moreover, the approach enables the identification of high amplitude co-fluctuations in fMRI time series. We found that normalized entropy is associated with stroke lesion severity and continually increases across the time of patients' recovery. Furthermore, high amplitude co-fluctuations not only relate to the lesion severity but are also associated with patients' level of recovery. The current study is the first edge-centric application for a clinical population in a longitudinal dataset and demonstrates how a different perspective for functional data analysis can further characterize topographic modulations of brain dynamics.

Abnormal brain functional and structural connectivity between the left supplementary motor area and inferior frontal gyrus in moyamoya disease

BACKGROUND

Disruption of brain functional connectivity has been detected after stroke, but whether it also occurs in moyamoya disease (MMD) is unknown. Impaired functional connectivity is always correlated with abnormal white matter fibers. Herein, we used multimodal imaging techniques to explore the changes in brain functional and structural connectivity in MMD patients.

METHODS

We collected structural images, resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging for each subject. Cognitive functions of MMD patients were evaluated using the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Trail Making Test parts A and B (TMT-A/-B). We calculated the functional connectivity for every paired region using 90 regions of interest from the Anatomical Automatic Labeling Atlas and then determined the differences between MMD patients and HCs. We extracted the functional connectivity of paired brain regions with significant differences between the two groups. Correlation analyses were then performed between the functional connectivity and variable cognitive functions. To explore whether the impaired functional connectivity and cognitive performances were attributed to the destruction of white matter fibers, we further analyzed fiber integrity using tractography between paired regions that were correlated with cognition.

RESULTS

There was lower functional connectivity in MMD patients as compared to HCs between the bilateral inferior frontal gyrus, between the bilateral supramarginal gyrus, between the left supplementary motor area (SMA) and the left orbital part of the inferior frontal gyrus (IFGorb), and between the left SMA and the left middle temporal gyrus (P < 0.01, FDR corrected). The decreased functional connectivity between the left SMA and the left IFGorb was significantly correlated with the MMSE (r = 0.52, P = 0.024), MoCA (r = 0.60, P = 0.006), and TMT-B (r = -0.54, P = 0.048) in MMD patients. White matter fibers were also injured between the SMA and IFGorb in the left hemisphere and were positively correlated with reduced functional connectivity.

CONCLUSIONS

Brain functional and structural connectivity between the supplementary motor area and inferior frontal gyrus in the left hemisphere are damaged in MMD. These findings could be useful in the evaluation of disease progression and prognosis of MMD.

The Value of Patient Registries to Advance Basic and Translational Research in the Area of Traumatic Brain Injury

The number of individuals affected by traumatic brain injury (TBI) is growing globally. TBIs may cause a range of physical, cognitive, and psychiatric deficits that can negatively impact employment, academic attainment, community independence, and interpersonal relationships. Although there has been a significant decrease in the number of injury related deaths over the past several decades, there has been no corresponding reduction in injury related disability over the same time period. We propose that patient registries with large, representative samples and rich multidimensional and longitudinal data have tremendous value in advancing basic and translational research and in capturing, characterizing, and predicting individual differences in deficit profile and outcomes. Patient registries, together with recent theoretical and methodological advances in analytic approaches and neuroscience, provide powerful tools for brain injury research and for leveraging the heterogeneity that has traditionally been cited as a barrier inhibiting progress in treatment research and clinical practice. We report on our experiences, and challenges, in developing and maintaining our own patient registry. We conclude by pointing to some future opportunities for discovery that are afforded by a registry model.

Post-Stroke Cognitive Impairment and Dementia

Poststroke cognitive impairment and dementia (PSCID) is a major source of morbidity and mortality after stroke worldwide. PSCID occurs as a consequence of ischemic stroke, intracerebral hemorrhage, or subarachnoid hemorrhage. Cognitive impairment and dementia manifesting after a clinical stroke is categorized as vascular even in people with comorbid neurodegenerative pathology, which is common in elderly individuals and can contribute to the clinical expression of PSCID. Manifestations of cerebral small vessel disease, such as covert brain infarcts, white matter lesions, microbleeds, and cortical microinfarcts, are also common in patients with stroke and likewise contribute to cognitive outcomes. Although studies of PSCID historically varied in the approach to timing and methods of diagnosis, most of them demonstrate that older age, lower educational status, socioeconomic disparities, premorbid cognitive or functional decline, life-course exposure to vascular risk factors, and a history of prior stroke increase risk of PSCID. Stroke characteristics, in particular stroke severity, lesion volume, lesion location, multiplicity and recurrence, also influence PSCID risk. Understanding the complex interaction between an acute stroke event and preexisting brain pathology remains a priority and will be critical for developing strategies for personalized prediction, prevention, targeted interventions, and rehabilitation. Current challenges in the field relate to a lack of harmonization of definition and classification of PSCID, timing of diagnosis, approaches to neurocognitive assessment, and duration of follow-up after stroke. However, evolving knowledge on pathophysiology, neuroimaging, and biomarkers offers potential for clinical applications and may inform clinical trials. Preventing stroke and PSCID remains a cornerstone of any strategy to achieve optimal brain health. We summarize recent developments in the field and discuss future directions closing with a call for action to systematically include cognitive outcome assessment into any clinical studies of poststroke outcome.

Neuropsychological evidence of multi-domain network hubs in the human thalamus

Hubs in the human brain support behaviors that arise from brain network interactions. Previous studies have identified hub regions in the human thalamus that are connected with multiple functional networks. However, the behavioral significance of thalamic hubs has yet to be established. Our framework predicts that thalamic subregions with strong hub properties are broadly involved in functions across multiple cognitive domains. To test this prediction, we studied human patients with focal thalamic lesions in conjunction with network analyses of the human thalamocortical functional connectome. In support of our prediction, lesions to thalamic subregions with stronger hub properties were associated with widespread deficits in executive, language, and memory functions, whereas lesions to thalamic subregions with weaker hub properties were associated with more limited deficits. These results highlight how a large-scale network model can broaden our understanding of thalamic function for human cognition.