Human restricted CHRFAM7A gene increases brain efficiency.

Introduction: CHRFAM7A, a uniquely human fusion gene, has been associated with neuropsychiatric disorders including Alzheimer's disease, schizophrenia, anxiety, and attention deficit disorder. Understanding the physiological function of CHRFAM7A in the human brain is the first step to uncovering its role in disease. CHRFAM7A was identified as a potent modulator of intracellular calcium and an upstream regulator of Rac1 leading to actin cytoskeleton reorganization and a switch from filopodia to lamellipodia implicating a more efficient neuronal structure. We performed a neurocognitive-MRI correlation exploratory study on 46 normal human subjects to explore the effect of CHRFAM7A on human brain. Methods: Dual locus specific genotyping of CHRFAM7A was performed on genomic DNA to determine copy number (TaqMan assay) and orientation (capillary sequencing) of the CHRFAM7A alleles. As only the direct allele is expressed at the protein level and affects α7 nAChR function, direct allele carriers and non-carriers are compared for neuropsychological and MRI measures. Subjects underwent neuropsychological testing to measure motor (Timed 25-foot walk test, 9-hole peg test), cognitive processing speed (Symbol Digit Modalities Test), Learning and memory (California Verbal Learning Test immediate and delayed recall, Brief Visuospatial Memory Test-Revised immediate and delayed recall) and Beck Depression Inventory-Fast Screen, Fatigue Severity Scale. All subjects underwent MRI scanning on the same 3 T GE scanner using the same protocol. Global and tissue-specific volumes were determined using validated cross-sectional algorithms including FSL's Structural Image Evaluation, using Normalization, of Atrophy (SIENAX) and FSL's Integrated Registration and Segmentation Tool (FIRST) on lesion-inpainted images. The cognitive tests were age and years of education-adjusted using analysis of covariance (ANCOVA). Age-adjusted analysis of covariance (ANCOVA) was performed on the MRI data. Results: CHRFAM7A direct allele carrier and non-carrier groups included 33 and 13 individuals, respectively. Demographic variables (age and years of education) were comparable. CHRFAM7A direct allele carriers demonstrated an upward shift in cognitive performance including cognitive processing speed, learning and memory, reaching statistical significance in visual immediate recall (FDR corrected p = 0.018). The shift in cognitive performance was associated with smaller whole brain volume (uncorrected p = 0.046) and lower connectivity by resting state functional MRI in the visual network (FDR corrected p = 0.027) accentuating the cognitive findings. Conclusion: These data suggest that direct allele carriers harbor a more efficient brain consistent with the cellular biology of actin cytoskeleton and synaptic gain of function. Further larger human studies of cognitive measures correlated with MRI and functional imaging are needed to decipher the impact of CHRFAM7A on brain function.

MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology.

Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principles and has contributed several classes to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.

An Automated Tool to Classify and Transform Unstructured MRI Data into BIDS Datasets.

The increasing use of neuroimaging in clinical research has driven the creation of many large imaging datasets. However, these datasets often rely on inconsistent naming conventions in image file headers to describe acquisition, and time-consuming manual curation is necessary. Therefore, we sought to automate the process of classifying and organizing magnetic resonance imaging (MRI) data according to acquisition types common to the clinical routine, as well as automate the transformation of raw, unstructured images into Brain Imaging Data Structure (BIDS) datasets. To do this, we trained an XGBoost model to classify MRI acquisition types using relatively few acquisition parameters that are automatically stored by the MRI scanner in image file metadata, which are then mapped to the naming conventions prescribed by BIDS to transform the input images to the BIDS structure. The model recognizes MRI types with 99.475% accuracy, as well as a micro/macro-averaged precision of 0.9995/0.994, a micro/macro-averaged recall of 0.9995/0.989, and a micro/macro-averaged F1 of 0.9995/0.991. Our approach accurately and quickly classifies MRI types and transforms unstructured data into standardized structures with little-to-no user intervention, reducing the barrier of entry for clinical scientists and increasing the accessibility of existing neuroimaging data.

Paramagnetic rim lesions predict greater long-term relapse rates and clinical progression over 10 years.

BACKGROUND: Paramagnetic rim lesions (PRLs) have been linked to higher clinical disease severity and relapse frequency. However, it remains unclear whether PRLs predict future, long-term disease progression. OBJECTIVES: The study aimed to assess whether baseline PRLs were associated with subsequent long-term (10 years) Expanded Disability Status Scale (EDSS) increase and relapse frequency and, if so, whether PRL-associated EDSS increase was mediated by relapse. METHODS: This retrospective analysis included 172 people with multiple sclerosis (pwMS) with 1868 yearly clinical visits over a mean follow-up time of 10.2 years. 3T magnetic resonance imaging (MRI) was acquired at baseline and PRLs were assessed on quantitative susceptibility mapping (QSM) images. The associations between PRLs, relapse, and rate of EDSS change were assessed using linear models. RESULTS: PRL+ pwMS had greater overall annual relapse rate (ß = 0.068; p = 0.010), three times greater overall odds of relapse (exp(ß) = 3.472; p = 0.009), and greater rate of yearly EDSS change (ß = 0.045; p = 0.010) than PRL- pwMS. Greater PRL number was associated with greater odds of at least one progression independent of relapse activity (PIRA) episode over follow-up (exp(ß) = 1.171, p = 0.009). Mediation analysis showed that the association between PRL presence (yes/no) and EDSS increase was 96.7% independent of relapse number. CONCLUSION: PRLs are a marker of aggressive ongoing disease inflammatory activity, including more frequent future clinical relapses and greater long-term, relapse-independent disability progression.

MRIO: The Magnetic Resonance Imaging Acquisition and Analysis Ontology.

Objective: Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. Materials and Methods: To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. Results: MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. Discussion: MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principals and has contributed several terms to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. Conclusion: MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.

Cortical thickness and cognition in older people with multiple sclerosis.

BACKGROUND: The structural changes associated with cognitive performance in older people with multiple sclerosis (PwMS; age ≥ 50 years old) remain unknown. OBJECTIVE: To determine the relationship between whole-brain (WBV), thalamus as the largest deep gray matter nuclei, and cortex-specific volume measurements with both cognitive impairment and numerical performance in older PwMS. The main hypothesis is that cognitive impairment (CI) in older PwMS is explained by cortical thinning in addition to global and thalamic neurodegenerative changes. METHODS: A total of 101 older PwMS underwent cognitive and neuroimaging assessment. Cognitive assessment included tests established as sensitive in MS samples (Minimal Assessment of Cognitive Function in MS; MACFIMS), as well as those tests often utilized in Alzheimer's dementia studies (Wechsler's Memory Scale, Boston Naming Test, Visual Motor Integration and language). Cognitive impairment (CI) was based on -1.5 standard deviations in at least 2 cognitive domains (executive function, learning and memory, spatial processing, processing speed and working memory and language) when compared to healthy controls. WBV and thalamic volume were calculated using SIENAX/FIRST and cortical thickness using FreeSurfer. Differences in cortical thickness between CI and cognitively preserved (CP) were determined using age, sex, education, depression and WBV-adjusted analysis of covariance (ANCOVA). The relationship between domain-specific cognitive performance and cortical thickness was analyzed by linear regression models adjusted for age, sex, education, depression, WBV and thalamic volume. Benjamini-Hochberg-adjusted p-values lower than 0.05 were considered significant. RESULTS: The average age of the study population was 62.6 (5.9) years old. After adjustment, CI PwMS had significantly thinner left fusiform (p = 0.0003), left inferior (p = 0.0032), left transverse (p = 0.0013), and bilateral superior temporal gyri (p = 0.002 and p = 0.0011) when compared to CP PwMS. After adjusting for age, sex, education, depression WBV, and thalamic volume, CI status was additionally predicted by the thickness of the left fusiform (p = 0.001) and left cuneus gyri (p = 0.004). After the adjustment, SDMT scores were additionally associated with left fusiform gyrus (p < 0.001) whereas letter-based verbal fluency performance with left pars opercularis gyrus (p < 0.001). CONCLUSION: In addition to global and thalamic neurodegenerative changes, the presence of CI in older PwMS is additionally explained by the thickness of multiple cortical regions.

Functional alteration due to structural damage is network dependent: insight from multiple sclerosis.

Little is known about how the brain's functional organization changes over time with respect to structural damage. Using multiple sclerosis as a model of structural damage, we assessed how much functional connectivity (FC) changed within and between preselected resting-state networks (RSNs) in 122 subjects (72 with multiple sclerosis and 50 healthy controls). We acquired the structural, diffusion, and functional MRI to compute functional connectomes and structural disconnectivity profiles. Change in FC was calculated by comparing each multiple sclerosis participant's pairwise FC to controls, while structural disruption (SD) was computed from abnormalities in diffusion MRI via the Network Modification tool. We used an ordinary least squares regression to predict the change in FC from SD for 9 common RSNs. We found clear differences in how RSNs functionally respond to structural damage, namely that higher-order networks were more likely to experience changes in FC in response to structural damage (default mode R2 = 0.160-0.207, P < 0.001) than lower-order sensory networks (visual network 1 R2 = 0.001-0.007, P = 0.157-0.387). Our findings suggest that functional adaptability to structural damage depends on how involved the affected network is in higher-order processing.

Functional Connectivity and Structural Disruption in the Default-Mode Network Predicts Cognitive Rehabilitation Outcomes in Multiple Sclerosis.

BACKGROUND AND PURPOSE: Efficacy of restorative cognitive rehabilitation can be predicted from baseline patient factors. In addition, patient profiles of functional connectivity are associated with cognitive reserve and moderate the structure-cognition relationship in people with multiple sclerosis (PwMS). Such interactions may help predict which PwMS will benefit most from cognitive rehabilitation. Our objective was to determine whether patient response to restorative cognitive rehabilitation is predictable from baseline structural network disruption and whether this relationship is moderated by functional connectivity. METHODS: For this single-arm repeated measures study, we recruited 25 PwMS for a 12-week program. Following magnetic resonance imaging, participants were tested using the Symbol Digit Modalities Test (SDMT) pre- and postrehabilitation. Baseline patterns of structural and functional connectivity were characterized relative to healthy controls. RESULTS: Lower white matter tract disruption in a network of region-pairs centered on the precuneus and posterior cingulate (default-mode network regions) predicted greater postrehabilitation SDMT improvement (P = .048). This relationship was moderated by profiles of functional connectivity within the network (R2 = .385, P = .017, Interaction ß = -.415). CONCLUSION: Patient response to restorative cognitive rehabilitation is predictable from the interaction between structural network disruption and functional connectivity in the default-mode network. This effect may be related to cognitive reserve.

Preserved network functional connectivity underlies cognitive reserve in multiple sclerosis.

Cognitive reserve is one's mental resilience or resistance to the effects of structural brain damage. Reserve effects are well established in people with multiple sclerosis (PwMS) and Alzheimer's disease, but the neural basis of this phenomenon is unclear. We aimed to investigate whether preservation of functional connectivity explains cognitive reserve. Seventy-four PwMS and 29 HCs underwent neuropsychological assessment and 3 T MRI. Structural damage measures included gray matter (GM) atrophy and network white matter (WM) tract disruption between pairs of GM regions. Resting-state functional connectivity was also assessed. PwMS exhibited significantly impaired cognitive processing speed (t = 2.14, p = .037) and visual/spatial memory (t = 2.72, p = .008), and had significantly greater variance in functional connectivity relative to HCs within relevant networks (p < .001, p < .001, p = .016). Higher premorbid verbal intelligence, a proxy for cognitive reserve, predicted relative preservation of functional connectivity despite accumulation of GM atrophy (standardized-ß = .301, p = .021). Furthermore, preservation of functional connectivity attenuated the impact of structural network WM tract disruption on cognition (ß = -.513, p = .001, for cognitive processing speed; ß = -.209, p = .066, for visual/spatial memory). The data suggests that preserved functional connectivity explains cognitive reserve in PwMS, helping to maintain cognitive capacity despite structural damage.

Response heterogeneity to home-based restorative cognitive rehabilitation in multiple sclerosis: An exploratory study.

BACKGROUND: Growing evidence supports the efficacy of restorative cognitive training in people with multiple sclerosis (PwMS), but the effects vary across individuals. Differences in treatment efficacy may be related to baseline individual differences. We investigated clinical characteristics and MRI variables to predict response to a previously validated approach to home-based restorative cognitive training. METHODS: In a single-arm repeated measures study, 51 PwMS completed a 12-week at-home restorative cognitive training program called BrainHQ, shown to be effective in a placebo-controlled clinical trial. Baseline demographic, clinical, neuropsychological, and brain MRI factors were captured and the effects of treatment were quantified with Symbol Digit Modalities Test (SDMT). Also measured were indices of treatment compliance. Regression modeling was employed to identify the factors associated with greatest SDMT improvement. RESULTS: As a group, patients improved significantly after training: mean SDMT improving from 49.6 ±â€¯14.7 to 52.6 ±â€¯15.6 (t = 3.91, p<0.001). Greater SDMT improvement correlated positively with treatment exposure (r = 0.38, p = 0.007). Increased post-rehabilitation improvement on SDMT was predicted by baseline relapsing-remitting course (ß=-0.34, p = 0.017), higher trait Conscientiousness-Orderliness (ß=0.29, p = 0.040), and higher baseline gray matter volume (GMV; ß=0.31, p = 0.030). CONCLUSION: The study was designed to explore the variables that predict favorable outcome in a home-based application of a validated restorative cognitive training program. We find good outcomes are most likely in patients with higher trait Conscientiousness-Orderliness, and relapsing-remitting course. The same was found for individuals with higher GMV. Future work in larger cohorts is needed to support these findings and to investigate the unique needs of individuals according to baseline factors.