Brain Connectivity

Introduction Understanding the mechanisms of human neuromaturation constitutes one of the fundamental questions of neuroscience. While it is well described that large-scale brain maturation is initiated within sensorimotor brain regions and progresses to associative cortex, the underlying developmental neurobiology remains to be fully characterized. Animal models have indicated that cortical inhibitory upregulation might be a driver of neurodevelopment. To investigate the hypothesis that cortical inhibitory upregulation plays a similar role in human neuromaturation, we developed a The Virtual Brain (TVB) based computational model (TVB-Child) to explore potential mechanisms of human neurodevelopment. Material and method We created neurodevelopmental dynamic brain network models capturing neurobiological maturation by using the large-scale brain simulator TVB and fitting brain network models to developmental functional MRI (fMRI) from the Human Connectome Project-Development (HCP-D) data set with 640 subjects with an age range of 6-21 years. Age-dependent trajectories in the fMRI data set were first analyzed by combined group-ICA/Dual Regression extracting subject-specific resting-state networks (RSN). Maturational topographical and topological redistribution of these networks were analyzed by linear and non-linear regression of RSN size and degree and strength centrality. Brain network models were fitted to the fMRI functional connectivity obtained from the HCP-D data set. Hypothesizing that cortical inhibition is a driver of neuromaturation, we analyzed spatiotemporal inhibition parameter gradients in the dynamic brain network model for the hypothesized significant correlations with fMRI RSN maturational trajectories. Results While during development frontoparietal (FP) and default mode network (DMN) grew and exhibited an increase in both degree and strength centrality, becoming dominant network hubs, the attention network underwent network pruning with a decrease in size and node degree. The primary sensory network changed little. For the fitted brain network models, we obtained a high degree of reproduction with correlation coefficients between empirical and simulated functional connectivities ranging between 0.80 and 0.95. Values of the feed forward inhibition model parameter wijFFI representing the strength of regional feedforward inhibitory input exhibited the most significant increase with age within the FP and DMN networks. A less pronounced, but significant, age-dependent increase of the inhibitory parameter values were seen in attention networks and no change within primary sensory networks. Conclusion Our study shows that high order (FP, DMN), attention and primary sensory networks exhibit distinct topographical and topological maturation trajectories. Moreover, brain network modeling revealed RSN-specific age-dependent inhibition trajectories, indicating that the model is able to reproduce and thus support candidate mechanisms of neurodevelopment.

BACKGROUND: Cognitive flexibility represents a crucial function in adapting to new environments. In this study we examined the ecological validity of a cognitive flexibility task by studying its relationship with individual traits (dogmatism, dependence on routines and perspective taking). Second, we investigated whether global and local structural brain connectivity properties were related to cognitive flexibility as well as associated traits and their possible age-related differences. METHOD: Thirty-eight young (18-35 years) and thirty-seven older (60-85 years) healthy participants took part in an MRI protocol including a Diffusion Weighted Imaging (DWI) sequence. Participants also performed a Rule-Switching task to measure cognitive flexibility performance and filled in questionnaires assessing dogmatism, dependence on routines and perspective taking. RESULTS: A higher cognitive flexibility was related to lower dogmatism and lower dependence on routines only in young adults. In relation to structural connectivity, we found that: a) global and local connectivity properties negatively predicted dogmatism levels in the full sample, b) local connectivity properties of the inferior frontal gyrus (IFG) positively predicted performance in cognitive flexibility performance in the full sample and in older adults, and c) connectivity between left inferior parietal lobule (IPL) and left putamen negatively predicted dogmatism in older adults. DISCUSSION: A deeper understanding of the shaping of structural networks supports a better understanding of cognitive flexibility and dogmatism in a highly dynamic world.

Background: Hallucinations, ranging from minor (MH) to structured, are a common non-motor symptom in Parkinson's disease (PD). Structured hallucinations have been associated with altered functional connectivity (FC) between dorsal/ventral attention (DAN, VAN) and default mode (DMN) networks. As structured hallucinations are linked to rapid cognitive decline and MH are often viewed as their precursor, it is imperative to understand the neural basis of MH, and its relationship with cognitive alterations. Objectives: We aimed to identify a whole-brain FC pattern associated with MH and alterations in attention-executive functioning in PD, leveraging a robotic procedure inducing presence hallucinations (riPH) experimentally, to which patients with hallucinations previously showed increased sensitivity. Methods: Non-demented PD patients (N = 53) were categorized into three subgroups based on their hallucination symptoms: no hallucinations (nH; n = 19), MH (n = 18), and structured hallucinations, with or without MH (SMH; n = 16). We combined results from the riPH procedure and neuropsychological tests and applied multivariate methods capturing their shared variance in resting-state fMRI data across the three subgroups. Results: We identified a distributed FC pattern more strongly expressed in patients with hallucinations (MH, SMH), and equally so across both groups, significantly associated with alterations in attention-executive functions and differences in riPH sensitivity. The pattern was primarily driven by FC between subcortical areas and visual network, DAN and DMN, and within-cerebellar and within-subcortical FC. Conclusions: Our results highlight the role of subcortical-cortical connectivity in PD hallucinations, associated with cognitive alterations and already present in less advanced MH patients.

Working memory (WM) is a core cognitive function that supports goal-directed behavior by temporarily maintaining and manipulating information. One of the most widely used paradigms for investigating WM function is the N-back task, and numerous neuroimaging studies have examined load-dependent neural responses using a variety of analytical approaches. However, most previous studies have focused on low-to-moderate load ranges (primarily 0-3-back), and it remains unclear how whole-brain activity patterns reconfigure across a broader range of WM demands, including conditions approaching capacity limits. In the present study, we investigated behavioral performance and whole-brain activity patterns across an extended N-back task ranging from 0-back to 7-back. Behavioral analyses revealed that discrimination sensitivity (d') decreased nonlinearly with increasing WM load, whereas reaction time (RT) exhibited an inverted-U pattern, peaking at intermediate load conditions. To characterize load-dependent whole-brain activity patterns, we computed relative activation maps by subtracting the participant-wise mean activation map across all conditions from each condition-specific activation map. Spatial similarity analyses with the Yeo 7-network templates revealed that low-load conditions showed relatively high similarity to default mode network (DMN)-related patterns. Similarity to the dorsal attention network (DAN) and frontoparietal network (FPN) was maximal at intermediate load levels, indicating load-dependent changes in network similarity profiles. High-load conditions were characterized by partial re-emergence of DMN-related patterns, accompanied by reduced DAN/FPN similarity. In addition, semantic similarity analysis using Neurosynth-derived semantic maps revealed relatively high similarity to default mode-related and self-referential representations under low-load conditions. Intermediate-load conditions showed strong correspondence with working memory- and executive control-related representations, whereas high-load conditions exhibited increased similarity to salience-, aversive/interoceptive-, and inhibitory-control-related representations. Together, these findings suggest that increasing WM load is associated not merely with stronger activation, but with changes in whole-brain activity patterns accompanied by nonlinear changes in network similarity profiles across levels of cognitive demand. Furthermore, the relative activation map-based whole-brain pattern analysis used in this study may provide a useful approach for evaluating changes in whole-brain state representations associated with cognitive load.

Introduction Parkinson's disease (PD) is a multifactorial disorder, affecting multiple neurotransmitter systems, including the cholinergic system. Cholinergic denervation is heterogeneous across patients and difficult to predict based on clinical presentation. In this study, we assessed the sensitivity of structural MRI (sMRI) and functional MRI (fMRI) to cholinergic degeneration related to PD and to cognitive functioning in PD. We compared our results to results from previously reported [18F]Fluoroethoxybenzovesamicol ([18F]FEOBV) PET imaging, which is considered the gold standard for cholinergic imaging. Methods 34 PD patients and 10 healthy controls underwent structural T1-weighted MRI. A subset of 14 patients and 9 controls also underwent resting-state fMRI. We extracted the bilateral volumes of the nucleus basalis of Meynert (NBM) from the sMRI images. Functional connectivity (FC) from the NBM to the cortex (NBM-FC) was determined using fMRI data. Principal component analysis (PCA) was applied to reduce the dimensionality of the NBM-FC images. We assessed performances for NBM-FC in distinguishing patients from controls using stepwise logistic regression. Similarly, NBM volume was used using logistic regression. Furthermore, the relation between these measures and cognitive function in several domains was investigated with (stepwise) linear regression. Leave-one-out cross validation (LOOCV) and bootstrapping was performed to assess robustness of the results. Results NBM-FC was well able to discriminate patients from controls with an AUC of 0.84 (95% CI: 0.62-1). NBM volume showed lower performance, but was still better than chance: AUC: 0.75 (95% CI: 0.57-0.93). Significant correlations were found between 1) cognition in the attentional domain and NBM-FC (r=0.63; p=.015) and 2) global cognition and NBM volume (r=0.55, p=.001). These results were inferior to those previously reported using [18F]FEOBV tracer uptake (see Chapter 6). Bootstrapping revealed that NBM volume of only the left hemisphere was stably related to PD diagnosis and global cognition in PD patients. We found that a lower NBM-FC in specific brain areas, including the fusiform gyrus, supramarginal gyrus and dorsolateral prefrontal cortex, was related to PD diagnosis. Bootstrapping revealed no stable NBM-FC pattern related to attention. Conclusion Although MRI results were slightly inferior to [18F]FEOBV PET data, MRI may provide a cheaper and more widely available alternative for cholinergic imaging. We recommend testing the utility of MRI as predictor and monitor of cholinergic treatment effect in a longitudinal study.

Individual brains are unique in structure and function. Functional differences are captured by neural fingerprints, which reflect individual differences in behavior and cognition as well as group-level changes related to neurodegenerative diseases. Most research efforts so far have focused on fingerprints com-prising full functional connectomes. However, the high dimensionality of the connectomes can increase computational load and impede performance of machine learning methods in potential applications. A low-dimensional alternative that retains individual features of the full connectomes would thus be beneficial. The present study employed latent-noise Bayesian Reduced Rank Regression (lnBRRR) to learn low-dimensional latent spaces that capture individual features in functional connectivity and power spectral density data derived from MEG recordings. LnBRRR performance was assessed with low training set sizes (N=20-44), and against principal component analysis and linear discriminant analysis. Model performance was also assessed with task data, and the solutions were compared across task conditions with cosine similarity to establish whether individual features are altered by different cognitive processes. LnBRRR captured generalizable individual patterns already at N=20 but N=30-35 was needed to reach optimal test accuracies and to prevent potential overfitting. The model also achieved comparable performance to the alternative models. Latent fingerprints derived from task data attained comparable performance to resting-state latent fingerprints, and lnBRRR solutions were shown to generalize across conditions. Additionally, the model solutions for power spectral density data were discovered to be notably similar, yet differently rotated, over task conditions, suggesting that similar patterns of individual features were captured by the model regardless of the task condition. Altogether, the present results highlight lnBRRR as a potential tool for neuroimaging data analysis and demonstrate that individual differences in power spectral density are largely intrinsic and unaffected by varying cognitive processes.

Quantitative MRI enables the detection of subtle microstructural alterations in normal-appearing white matter (NAWM) associated with pathological conditions such as multiple sclerosis. Quantitative metrics including R1, R2, and the bSSFP asymmetry index (AI) were evaluated in the WM of 20 relapsing-remitting multiple sclerosis (RRMS) patients and 10 healthy controls (HC). A multi-parametric frame-work based on a phase-cycled balanced steady-state free precession (pc-bSSFP) sequence was used. Diffusion tensor imaging-derived measures, including fiber-to-field angle, number of fiber orientations, and fractional anisotropy, were incorporated to assess parameter anisotropy. Statistical analysis was performed using linear mixed-effects models to test for group, ROI, and group-by-ROI effects for each metric, with ROI-specific group comparisons derived from the model. Significant main effects of group, ROI, and group-by-ROI interaction were observed for both R1 and R2, whereas for AI only the ROI effect reached significance (group p= 0.462; group-by-ROI p = 0.786). Fourteen of sixteen ROIs demonstrated significantly lower R1 and R2 values in RRMS compared with HC. No ROI showed significant differences in AI. In conclusion, pc-bSSFP-based relaxometry reveals predominantly white matter alterations in RRMS, while enabling a comprehensive whole-brain assessment that also encompasses gray matter.

Moderate-to-severe traumatic brain injury (msTBI) is recognized as a chronic and evolving neurological condition characterized by progressive structural brain changes and persistent cognitive impairment. While prior studies have demonstrated widespread atrophy following msTBI, less is known regarding the longitudinal trajectory of gray matter (GM) changes during recovery and post-rehabilitation. The current study used longitudinal voxel-based morphometry (VBM) to characterize GM volume changes over a period of 9 months, in individuals with msTBI relative to healthy controls (HC). Associations between regional GM volume and neuropsychological functioning were examined. Twenty-eight participants (14 msTBI, 14 HC) completed MRI and neuropsychological assessments across three timepoints spanning outpatient rehabilitation and follow-up. Longitudinal VBM analyses revealed significant group and time interactions within subcortical and limbic regions. Relative to HC, individuals with msTBI showed lower GM volume in these regions at baseline, with trajectories that converged toward HC values (right hippocampus) or increased relative to HC over the rehabilitation period (bilateral pulvinar), whereas the right amygdala and inferior cerebellar vermis remained persistently reduced. Significant longitudinal improvements in memory and psychomotor speed during the rehabilitation period were demonstrated in msTBI. Greater (preserved) GM volume within the right hippocampus, thalamus, and bilateral pulvinar was associated with better performance across measures of verbal memory, processing speed, executive functioning, and cognitive flexibility. These findings suggest that msTBI is associated with dynamic structural brain changes involving subcortical, limbic, and cerebellar networks, and that the rehabilitation period was accompanied by relative volumetric stabilization in these regions and by meaningful cognitive improvement.

A central challenge in neuroscience is understanding how the human brain is organised to support optimal functioning and adaptability. One approach to characterise complex brain dynamics is by artificially perturbing whole-brain models. Here, we asked whether whole-brain organisation under perturbation in major depressive disorder (MDD) changes after intervention with psilocybin and escitalopram. First, we built whole-brain models of pre- and post-treatment resting-state functional magnetic resonance imaging (fMRI) and obtained an initial generative effective connectivity (GEC) matrix for each individual. Then, we employed systematic and local artificial perturbations across intensities, re-optimised each model to create a response GEC (GECr), and assessed the extent of brain reorganisation by quantifying the brain network reconfiguration index (NRI). Our results showed that the global brain NRI increases with psilocybin and decreases with escitalopram. Across sessions and interventions, higher global NRI was related with localised perturbations in brain areas orchestrating the brain's hierarchical dynamics. Traditional approaches complemented our investigation. Our findings suggest distinct neural changes following each treatment for MDD. The increase in brain reorganisation under perturbation following psilocybin is consistent with greater brain flexibility and changeability, whereas the decrease following escitalopram suggests more stabilised brain dynamics. Overall, perturbation-induced brain NRI may represent a useful approach for uncovering neural changes following different interventions for depression.

Autism is a heterogeneous neurodevelopmental condition, often accompanied by challenges in language and cognitive development. Although atypical functional connectivity (FC) has been reported in autism, the timing of when it first emerges and its relevance for later behavior remain poorly understood. In this study, we examined developmental trajectories of alpha-band FC and network organization across the first three years of life. We computed global alpha-band measures, including peak alpha connectivity frequency (PACF), mean FC, clustering coefficient, and modularity, to characterize nonlinear developmental trajectories from longitudinal EEGs collected from 238 children (3-to-36-month-olds) with (Autism; n=58) and without (LL-noAutism; n=180) autism. Network-based statistics (NBS-Predict) identified subnetworks contributing to group differences at each age. Exploratory graph analyses (EGA) examined associations among FC, network measures, and language outcomes. We observed that PACF increased linearly with age in both groups. Global alpha-band connectivity measures showed a similar developmental pattern, with mean global FC, clustering coefficient, and modularity all increasing rapidly during the first year in both groups. Thereafter, these measures declined in the Autism group but continued to gradually increase in the LL-noAutism group. Compared to LL-noAutism, NBS-Predict identified both hyper- and hypo-connectivity subnetworks in Autism at 3 months, followed by a hypo-connectivity subnetwork at 24 and 36 months. EGA indicated that early hyperconnectivity predicted later hypoconnectivity and was associated with subsequent network organization and language outcomes. These findings indicate that altered alpha-band connectivity trajectories are detectable in infancy in children later diagnosed with autism and may contribute to later differences in developmental outcomes.

Diffusion MRI (dMRI) enables assessment of white matter microstructural abnormalities in Alzheimers disease (AD), and multisite datasets enable more robust modeling of non-biological variation that can confound analyses. The Alzheimers Disease Neuroimaging Initiative (ADNI) includes over 10 dMRI protocols, necessitating robust methods to model protocol-related variability when pooling data. Here, we compared three harmonization approaches: (1) mixed-effects models, (2) ComBat-GAM, and (3) eHarmonize, a reference-based lifespan method. We assessed their ability to reduce protocol-related variability in diffusion tensor imaging fractional anisotropy (FA) and mean diffusivity (MD) while preserving associations with cognitive impairment (CI), and amyloid-beta (A{beta}) and tau PET burden in 1,086 ADNI3/4 participants. All approaches yielded more closely aligned FA/MD distributions across protocols. Associations with clinical indicators of CI were highly consistent across approaches, whereas PET associations were less widespread and more variable. Overall, multiple strategies effectively modeled protocol-related variability while preserving AD-related associations.

Multimodal imaging approaches that combine structural and functional neuroimaging provide a robust framework for examining neuroplastic adaptations that may not be captured by any single modality. The present study investigated the effects of a four-week expiratory muscle strength training (EMST) program on structural and resting-state functional connectivity in healthy young adults. Five healthy young adult males (aged 19-35 years) completed a standard four-week EMST protocol and underwent pre- and post-training imaging assessments. Structural neuroimaging included T1-weighted and diffusion-weighted MRI, which were analyzed using voxel-based morphometry, surface-based morphometry, and white-matter structural connectivity. Functional neuroimaging consisted of resting-state fMRI to assess training-related changes in functional architecture, network connectivity, and global network measures. Structural MRI analyses revealed no significant changes in gray or white matter volume, cortical morphology, or white-matter structural connectivity following EMST (all FWE- or FDR-corrected p > .05). In contrast, resting-state fMRI demonstrated a significant increase in whole-brain functional connectivity (FDR-corrected p = .036), accompanied by greater network integration, reflected in increased local efficiency and transitivity and reduced modularity. Network-level analyses showed enhanced within- and between-network connectivity in sensorimotor and cognitive circuits. Our findings demonstrate robust functional reorganization following EMST, despite the absence of detectable macrostructural or large-scale white-matter connectivity changes, at least within the timescale and sample characteristics of the current study. These results reflect early-stage neuroplasticity, both globally and within the networks underlying speech and swallowing control and suggest that functional reorganization occurs early in training and likely precedes longer-term structural modifications in these networks.

Background. Cognitive impairment in Parkinson's disease (PD) is highly prevalent and heterogeneous. Assessing multiple cognitive domains is challenging and risks redundancy. This study evaluated whether a discriminant analysis approach could optimize the selection of specific tasks and measures for identifying attention and memory deficits in PD. Methods. Thirty PD patients and 25 cognitively unimpaired (CU) controls completed four experimental tasks: two assessing attention (flanker and spatial Stroop), one for recognition memory, one for working memory (n-back). Following group-level difference analyses, a discriminant analysis was performed to identify which tasks, and performance metrics possessed the highest sensitivity for distinguishing PD patients from CU individuals. Results. At the group level, PD patients exhibited significantly worse conflict costs in both attention tasks and lower sensitivity scores (d') in the recognition memory task compared to CU controls. The discriminant analysis revealed that time-based measures from the spatial Stroop task and the sensitivity score from the recognition memory task provided the highest discriminating power to differentiate between the two groups. Conclusion. These findings suggest that cognitive deficits in PD can be identified with high diagnostic accuracy using a targeted subset of metrics, eliminating the need for extensive and redundant neuropsychological testing batteries for attention and memory, without needing an extensive number of cognitive tasks for attention and memory.

While Blood Oxygenation Level-Dependent (BOLD) fMRI remains the gold standard for mapping functional brain networks with MRI, its vascular origins inherently conflate haemodynamic effects with neural activity, limiting its sensitivity in white matter (WM) or its interpretation in neurovascular diseases. Apparent Diffusion Coefficient (ADC) fMRI offers an alternative, diffusion-based contrast that is theoretically more sensitive to neuromorphological coupling and therefore more specific to neuronal activation, though investigated primarily during task-based conditions. This study aimed to comprehensively evaluate the efficacy of isotropic ADC-fMRI in detecting established resting-state networks (RSNs) and to extend this methodology to the investigation of grey-to-white matter (GM-WM) functional connectivity. Our analyses revealed a gradient of ADC detectability shaped by the degree of static functional cohesion and structural tethering of each network. The visual and somatomotor networks, being both highly segregated and strongly anchored to underlying structural pathways, yielded the most robust detection. The default mode network (DMN) and dorsal attention network (DAN) reached group-level significance but with lower effect sizes, and their detection proved fragile across analytical approaches. The frontoparietal network (FPN) and salience network (SAN), whose functional identity is defined by dynamic cross-network reconfiguration, did not reach significance. This gradient partially mirrors the established hierarchy of network segregation observed in BOLD, while further suggesting that ADC sensitivity depends on the structural grounding of each network. Furthermore, ADC demonstrated superior sensitivity to GM-WM functional coupling compared to BOLD. GM-WM functional connectivity profiles derived from ADC were significantly more aligned with underlying structural WM architecture across subjects. Taken together, these findings position isotropic ADC-fMRI as a viable complementary modality to BOLD, offering a more direct window into the neural and structural foundations of brain connectivity.

Introduction- Choroid plexus (CP) enlargement on brain MRI has been identified as an emerging neuroinflammatory biomarker in multiple sclerosis (MS), yet its relationship to downstream parenchymal neurochemical abnormalities remains unknown. Proton magnetic resonance spectroscopy (1H MRS) enables non-invasive in vivo quantification of neurometabolites, making it well-suited to probe downstream consequences of CP pathology in MS. Methods- Ultra-high-field 7T 1H MRS was performed in 45 people with MS (pwMS) (28 Relapsing Remitting MS, RRMS; 17 Progressive MS, PMS) and 43 age- and sex-matched healthy controls (HCs) in the posterior cingulate cortex (PCC) and centrum semiovale white matter (CSWM). CP volume, EDSS, and MS Functional Composite measures were also acquired. Group differences in metabolite concentrations were evaluated using Mann-Whitney U tests with correction for multiple comparisons, and associations between CP volume, altered metabolites, and clinical disability and functional measures were investigated. Results- Myo-inositol (mI) was significantly elevated and total N-acetylaspartate was reduced in both MS subtypes, in the CSWM. In PMS, CP volume was positively associated with CSWM mI/total creatine (tCr) ({rho} = 0.63, p = 0.008), an association absent in RRMS. Across the combined MS cohort, CP volume correlated significantly with EDSS ({rho} = 0.40, p = 0.006). Conclusions- WM mI/tCr was elevated and tNAA/tCr was reduced across MS phenotypes compared with controls, reflecting a dual metabolic signature consistent with concurrent glial overactivation and neuroaxonal compromise. Increased CP volume was associated with greater neurological disability across MS phenotypes. The association of CP enlargement with CSWM mI/tCr in PMS suggests a potential link between CP-mediated periventricular inflammation and progressive WM glial pathology. Collectively, these findings support CP volume as a clinically relevant, non-invasive biomarker and restoring CP integrity as a potential therapeutic target in PMS, where effective treatments remain limited.

Aphasia diagnosis in Indonesia remains challenging due to limited culturally and linguistically appropriate instruments. Widely used tools such as the Boston Diagnostic Aphasia Examination (BDAE) and Western Aphasia Battery (WAB) are not adapted to the Indonesian context, while Tes Afasia untuk Diagnosis, Informasi, dan Rehabilitasi (TADIR) provides screening but lacks diagnostic accuracy. To address this gap, we developed the Instrumen Diagnosis dan Evaluasi Afasia (IDEA) for native Indonesian speakers and evaluated its validity, reliability, and normative cutoff values in cognitively healthy Indonesian adults. Eighty-three cognitively normal adults (screened using MoCA-Ina) with no history of neurological disease were assessed using IDEA, which evaluates six language domains. Items were adapted from existing tools and reviewed by experts. Content validity, internal consistency (Cronbachs alpha), and construct validity (Exploratory Factor Analysis) were analyzed using SPSS v25. A total of 83 participants were included (median age = 55.81 years, 54% secondary education). IDEA demonstrated good feasibility, with an average completion time of 45-60 minutes depending on participant engagement. Content validity was established by unanimous expert consensus. Construct validity showed meritorious sampling adequacy (KMO = .872) and significant sphericity (Bartletts test {chi}^2 (15) = 278.523, p<.001), supporting factor analysis. Internal consistency showed good reliability across six domains (Cronbachs = 0.896). IDEA is a valid and reliable tool for assessing aphasia in Indonesian natives. It is a culturally appropriate assessment tool which offers structured, domain-based evaluation and supports differential diagnosis of both classical and progressive aphasia syndromes. Keywords: Aphasia, Language Assessment, Indonesian, IDEA, Validity

INTRODUCTIONDistinguishing healthy brain aging from early neurodegenerative disruption remains a major challenge in Alzheimers disease. METHODSUsing resting-state fMRI from the Alzheimers Disease Neuroimaging Initiative (357 participants; cognitively normal, mild cognitive impairment, Alzheimers disease), we trained a Hidden Markov Model exclusively on cognitively normal adults to define latent connectivity states. A generalized additive model estimated an age-adjusted reference trajectory of transition entropy, and subject-specific deviations from this trajectory were quantified. RESULTSMild cognitive impairment and Alzheimers disease showed progressively greater deviation from the normative reference, with the strongest disruption in Alzheimers disease. A single absolute-deviation score retained much of the predictive information contained in higher-dimensional dynamic features. DISCUSSIONThese findings suggest Alzheimers disease is associated with measurable departure from healthy dynamic brain aging, providing an interpretable framework for future longitudinal and clinically validated studies.

Background The field of implantable Brain-Computer Interfaces (iBCIs) is rapidly advancing, with individuals with amyotrophic lateral sclerosis (ALS) as key beneficiaries. However, ALS-related cortical degeneration may impair iBCI effectiveness. This study investigated whether structural magnetic resonance imaging (MRI) and functional MRI (fMRI) metrics are associated with the quality of electrocorticography (ECoG) signals critical for iBCI use. Methods Six late-stage ALS participants and 76 controls underwent T1-weighted structural MRI and task-based fMRI during right-hand movement or attempts thereof. ECoG data of ALS participants was benchmarked using ECoG data acquired in epilepsy patients. Grey matter thickness in the sensorimotor cortex and fMRI activation in the motor-hand area were measured. Results Four ALS participants showed >0.4 mm thinning in the precentral gyrus, while the postcentral gyrus was spared. ECoG signal quality was significantly associated with precentral grey matter thickness, but not with fMRI activity. Conclusions These findings suggest that presurgical assessment of precentral grey matter thickness could potentially prove useful for iBCI candidate selection in advanced ALS.

Background: The ability to remember object locations in real life is a fundamental cognitive process that supports goal-directed behavior and is particularly vulnerable to aging and neurodegenerative disease. Despite a growing body of functional magnetic resonance imaging (fMRI) research on object-location memory (OLM), the neural substrates of establishing and retrieving location information are largely unknown. Objective: This systematic review and coordinate-based meta-analysis aimed to identify brain regions consistently activated during OLM in healthy adults, primarily for encoding and - on an exploratory basis - for retrieval, and to characterize age-related differences in OLM-related neural activity. Methods: A systematic search was conducted across three databases (PubMed, PsycInfo, Cochrane Library) up to February 2026. Studies employing task-based fMRI during the encoding and retrieval of object-location associations in healthy adults were eligible. Age-related differences in OLM-related brain activity were examined via narrative synthesis. An activation likelihood estimation (ALE) meta-analysis was performed on studies reporting stereotactic peak coordinates. The review was pre-registered on PROSPERO (CRD420251023695). Results: Twenty-one studies comprising 637 participants were included in the systematic review, with 12 studies being eligible for the encoding ALE meta-analysis. The retrieval ALE meta-analysis was not possible due to the limited number of included studies and reported foci. The systematic review indicated that OLM encoding consistently recruited bilateral fusiform gyri and parahippocampal cortices, with additional engagement of parietal and prefrontal regions across individual studies, whereas OLM retrieval recruited mainly the hippocampus and precuneus. The coordinate-based ALE meta-analysis revealed two significant clusters of activation during OLM encoding: a left-lateralized cluster encompassing the fusiform gyrus, parahippocampal gyrus, and inferior temporal gyrus (peak MNI: -28, -38, -16), and a right-hemisphere cluster spanning the parahippocampal gyrus and fusiform gyrus (peak MNI: 30, -46, -16). Age-related differences, based on a small number of studies with direct age comparison, pointed toward reduced activity in posterior cortical regions coupled with increased activity in prefrontal and midline regions. Additionally, younger adults showed greater hippocampal activation for successful than unsuccessful spatial retrieval, whereas older adults showed the opposite pattern. Conclusion: The systematic review and meta-analysis identify the fusiform gyri and parahippocampal cortices as the most reliably activated regions during OLM encoding, locating OLM formation primarily within the ventral visual-to-medial-temporal processing stream. Retrieval additionally engaged the hippocampus and precuneus, consistent with their established roles in episodic memory. Age-related differences included reduced posterior cortical encoding activity in older adults, a reversal of the hippocampal activation pattern during retrieval, and weaker suppression of midline regions during task performance. The identified encoding pathway may inform targeted network-level interventions such as non-invasive brain stimulation to counteract cognitive decline in aging and neurodegenerative disease.

Background: Serum neurofilament light chain (NfL) indexes axonal injury and glial fibrillary acidic protein (GFAP) astrocytic pathology in multiple sclerosis (MS). GFAP rises disproportionately as relapsing-remitting MS (RRMS) shifts to progressive forms on research-grade SIMOA. The commercial Roche Elecsys ECLIA platform reads six-fold lower and is undescribed across subtypes. Objective: To describe both markers by MS subtype on ECLIA. Methods: Retrospective single-center analysis of 603 MS patients (2022-2026). NfL and GFAP were measured by LabCorp Roche Elecsys ECLIA; subtype came from ICD-10 codes and notes. We examined both markers by subtype, their correlation, and NfL against gadolinium-enhancing (Gd+) MRI lesions. Results: Median NfL was 1.32 pg/mL (IQR 1.01-1.91). Both rose with stage, steeper for GFAP: NfL 1.18 (RRMS), 1.54 (SPMS, p<0.001), 1.78 (PPMS, p=0.001); GFAP 41.90, 63.80 (p<0.0001), 75.75 (p=0.08, n=6). SPMS and PPMS GFAP did not differ (p=0.83). The markers correlated moderately (r=0.569). Of 34 Gd+ encounters with NfL within 30 days, 3 (9%) were elevated. Conclusion: On ECLIA, both markers rose with MS stage, GFAP more steeply, and both progressive subtypes exceeded RRMS. NfL rarely flagged a recent Gd+ lesion, consistent with its delayed kinetics. The two index distinct processes and reproduce on an orderable assay a profile once confined to research-grade SIMOA.