Structural connectivity changes in unilateral hearing loss.

Network connectivity, as mapped by the whole brain connectome, plays a crucial role in regulating auditory function. Auditory deprivation such as unilateral hearing loss might alter structural network connectivity; however, these potential alterations are poorly understood. Thirty-seven acoustic neuroma patients with unilateral hearing loss (19 left-sided and 18 right-sided) and 19 healthy controls underwent diffusion-weighted and T1-weighted imaging to assess edge strength, node strength, and global efficiency of the structural connectome. Edge strength was estimated by pair-wise normalized streamline density from tractography and connectomics. Node strength and global efficiency were calculated through graph theory analysis of the connectome. Pure-tone audiometry and word recognition scores were used to correlate the degree and duration of unilateral hearing loss with node strength and global efficiency. We demonstrate significantly stronger edge strength and node strength through the visual network, weaker edge strength and node strength in the somatomotor network, and stronger global efficiency in the unilateral hearing loss patients. No discernible correlations were observed between the degree and duration of unilateral hearing loss and the measures of node strength or global efficiency. These findings contribute to our understanding of the role of structural connectivity in hearing by facilitating visual network upregulation and somatomotor network downregulation after unilateral hearing loss.

Tau follows principal axes of functional and structural brain organization in Alzheimer's disease.

Alzheimer's disease (AD) is a brain network disorder where pathological proteins accumulate through networks and drive cognitive decline. Yet, the role of network connectivity in facilitating this accumulation remains unclear. Using in-vivo multimodal imaging, we show that the distribution of tau and reactive microglia in humans follows spatial patterns of connectivity variation, the so-called gradients of brain organization. Notably, less distinct connectivity patterns ("gradient contraction") are associated with cognitive decline in regions with greater tau, suggesting an interaction between reduced network differentiation and tau on cognition. Furthermore, by modeling tau in subject-specific gradient space, we demonstrate that tau accumulation in the frontoparietal and temporo-occipital cortices is associated with greater baseline tau within their functionally and structurally connected hubs, respectively. Our work unveils a role for both functional and structural brain organization in pathology accumulation in AD, and supports subject-specific gradient space as a promising tool to map disease progression.

End-stage renal disease accompanied by mild cognitive impairment: A study and analysis of trimodal brain network fusion.

The function and structure of brain networks (BN) may undergo changes in patients with end-stage renal disease (ESRD), particularly in those accompanied by mild cognitive impairment (ESRDaMCI). Many existing methods for fusing BN focus on extracting interaction features between pairs of network nodes from each mode and combining them. This approach overlooks the correlation between different modal features during feature extraction and the potentially valuable information that may exist between more than two brain regions. To address this issue, we propose a model using a multi-head self-attention mechanism to fuse brain functional networks, white matter structural networks, and gray matter structural networks, which results in the construction of brain fusion networks (FBN). Initially, three networks are constructed: the brain function network, the white matter structure network, and the individual-based gray matter structure network. The multi-head self-attention mechanism is then applied to fuse the three types of networks, generating attention weights that are transformed into an optimized model. The optimized model introduces hypergraph popular regular term and L1 norm regular term, leading to the formation of FBN. Finally, FBN is employed in the diagnosis and prediction of ESRDaMCI to evaluate its classification performance and investigate the correlation between discriminative brain regions and cognitive dysfunction. Experimental results demonstrate that the optimal classification accuracy achieved is 92.80%, which is at least 3.63% higher than the accuracy attained using other methods. This outcome confirms the effectiveness of our proposed method. Additionally, the identification of brain regions significantly associated with scores on the Montreal cognitive assessment scale may shed light on the underlying pathogenesis of ESRDaMCI.

Glioblastoma and brain connectivity: the need for a paradigm shift.

Despite substantial advances in cancer treatment, for patients with glioblastoma prognosis remains bleak. The emerging field of cancer neuroscience reveals intricate functional interplays between glioblastoma and the cellular architecture of the brain, encompassing neurons, glia, and vessels. New findings underscore the role of structural and functional connections within hierarchical networks, known as the connectome. These connections contribute to the location, spread, and recurrence of a glioblastoma, and a patient's overall survival, revealing a complex interplay between the tumour and the CNS. This mounting evidence prompts a paradigm shift, challenging the perception of glioblastomas as mere foreign bodies within the brain. Instead, these tumours are intricately woven into the structural and functional fabric of the brain. This radical change in thinking holds profound implications for the understanding and treatment of glioblastomas, which could unveil new prognostic factors and surgical strategies and optimise radiotherapy. Additionally, a connectivity approach suggests that non-invasive brain stimulation could disrupt pathological neuron-glioma interactions within specific networks.

Disrupted individual-level morphological brain network in spinal muscular atrophy types 2 and 3.

BACKGROUND AND OBJECTIVE: Spinal muscular atrophy (SMA) is one of the most common monogenic neuromuscular diseases, and the pathogenesis mechanisms, especially the brain network topological properties, remain unknown. This study aimed to use individual-level morphological brain network analysis to explore the brain neural network mechanisms in SMA. METHODS: Individual-level gray matter (GM) networks were constructed by estimating the interregional similarity of GM volume distribution using both Kullback-Leibler divergence-based similarity (KLDs) and Jesen-Shannon divergence-based similarity (JSDs) measurements based on Automated Anatomical Labeling 116 and Hammersmith 83 atlases for 38 individuals with SMA types 2 and 3 and 38 age- and sex-matched healthy controls (HCs). The topological properties were analyzed by the graph theory approach and compared between groups by a nonparametric permutation test. Additionally, correlation analysis was used to assess the associations between altered topological metrics and clinical characteristics. RESULTS: Compared with HCs, although global network topology remained preserved in individuals with SMA, brain regions with altered nodal properties mainly involved the right olfactory gyrus, right insula, bilateral parahippocampal gyrus, right amygdala, right thalamus, left superior temporal gyrus, left cerebellar lobule IV-V, bilateral cerebellar lobule VI, right cerebellar lobule VII, and vermis VII and IX. Further correlation analysis showed that the nodal degree of the right cerebellar lobule VII was positively correlated with the disease duration, and the right amygdala was negatively correlated with the Hammersmith Functional Motor Scale Expanded (HFMSE) scores. CONCLUSIONS: Our findings demonstrated that topological reorganization may prioritize global properties over nodal properties, and disrupted topological properties in the cortical-limbic-cerebellum circuit in SMA may help to further understand the network pathogenesis underlying SMA.

Neuronal Circuit Dysfunction in Amyotrophic Lateral Sclerosis.

The primary neural circuit affected in Amyotrophic Lateral Sclerosis (ALS) patients is the corticospinal motor circuit, originating in upper motor neurons (UMNs) in the cerebral motor cortex which descend to synapse with the lower motor neurons (LMNs) in the spinal cord to ultimately innervate the skeletal muscle. Perturbation of these neural circuits and consequent loss of both UMNs and LMNs, leading to muscle wastage and impaired movement, is the key pathophysiology observed. Despite decades of research, we are still lacking in ALS disease-modifying treatments. In this review, we document the current research from patient studies, rodent models, and human stem cell models in understanding the mechanisms of corticomotor circuit dysfunction and its implication in ALS. We summarize the current knowledge about cortical UMN dysfunction and degeneration, altered excitability in LMNs, neuromuscular junction degeneration, and the non-cell autonomous role of glial cells in motor circuit dysfunction in relation to ALS. We further highlight the advances in human stem cell technology to model the complex neural circuitry and how these can aid in future studies to better understand the mechanisms of neural circuit dysfunction underpinning ALS.

Topological alterations in white matter anatomical networks in cervical dystonia.

BACKGROUND: Accumulating neuroimaging evidence indicates that patients with cervical dystonia (CD) have changes in the cortico-subcortical white matter (WM) bundle. However, whether these patients' WM structural networks undergo reorganization remains largely unclear. We aimed to investigate topological changes in large-scale WM structural networks in patients with CD compared to healthy controls (HCs), and explore the network changes associated with clinical manifestations. METHODS: Diffusion tensor imaging (DTI) was conducted in 30 patients with CD and 30 HCs, and WM network construction was based on the BNA-246 atlas and deterministic tractography. Based on the graph theoretical analysis, global and local topological properties were calculated and compared between patients with CD and HCs. Then, the AAL-90 atlas was used for the reproducibility analyses. In addition, the relationship between abnormal topological properties and clinical characteristics was analyzed. RESULTS: Compared with HCs, patients with CD showed changes in network segregation and resilience, characterized by increased local efficiency and assortativity, respectively. In addition, a significant decrease of network strength was also found in patients with CD relative to HCs. Validation analyses using the AAL-90 atlas similarly showed increased assortativity and network strength in patients with CD. No significant correlations were found between altered network properties and clinical characteristics in patients with CD. CONCLUSION: Our findings show that reorganization of the large-scale WM structural network exists in patients with CD. However, this reorganization is attributed to dystonia-specific abnormalities or hyperkinetic movements that need further identification.

Altered morphometric similarity networks in insomnia disorder.

Previous studies on structural covariance network (SCN) suggested that patients with insomnia disorder (ID) show abnormal structural connectivity, primarily affecting the somatomotor network (SMN) and default mode network (DMN). However, evaluating a single structural index in SCN can only reveal direct covariance relationship between two brain regions, failing to uncover synergistic changes in multiple structural features. To cover this research gap, the present study utilized novel morphometric similarity networks (MSN) to examine the morphometric similarity between cortical areas in terms of multiple sMRI parameters measured at each area. With seven T1-weighted imaging morphometric features from the Desikan-Killiany atlas, individual MSN was constructed for patients with ID (N = 87) and healthy control groups (HCs, N = 84). Two-sample t-test revealed differences in MSN between patients with ID and HCs. Correlation analyses examined associations between MSNs and sleep quality, insomnia symptom severity, and depressive symptoms severity in patients with ID. The right paracentral lobule (PCL) exhibited decreased morphometric similarity in patients with ID compared to HCs, mainly manifested by its de-differentiation (meaning loss of distinctiveness) with the SMN, DMN, and ventral attention network (VAN), as well as its decoupling with the visual network (VN). Greater PCL-based de-differentiation correlated with less severe insomnia and fewer depressive symptoms in the patients group. Additionally, patients with less depressive symptoms showed greater PCL de-differentiation from the SMN. As an important pilot step in revealing the underlying morphometric similarity alterations in insomnia disorder, the present study identified the right PCL as a hub region that is de-differentiated with other high-order networks. Our study also revealed that MSN has an important potential to capture clinical significance related to insomnia disorder.

Brain Network Characterization of Preterm Infants With Bronchopulmonary Dysplasia.

BACKGROUND: Bronchopulmonary dysplasia (BPD) affects the microstructure of white matter in preterm infants, but its influence on the changes of the brain structural network has not been elaborated. This study aims to investigate the connectivity characteristics of the brain structural network of BPD by using diffusion tensor imaging. METHODS: Thirty-three infants with BPD and 26 infants without BPD were enrolled in this study. Brain structural networks were constructed utilizing automated anatomic labeling mapping by tracing the fibers between each pair of regions in individual space. We calculated network metrics such as global efficiency, local efficiency, clustering coefficients, characteristic path length, and small-worldness. Then we compared the network metrics of these infants with those of 57 healthy term infants of comparable postmenstrual age at magnetic resonance imaging scan. Finally, network-based statistics was used to analyze the differences in brain network connectivity between the groups with and without BPD. RESULTS: Preterm infants with BPD had higher local efficiency and clustering coefficient, lower global efficiency, and longer characteristic path length. Also, preterm infants with BPD had decreased strength of limbic connections mainly in four brain regions: the left lingual gyrus, the left calcarine fissure and surrounding cortex, the right parahippocampal gyrus, and the left precuneus. CONCLUSIONS: Our findings suggest that preterm infants with BPD have lower network integration and higher segregation at term-equivalent age, which may reflect a compensatory mechanism. In addition, BPD affects brain regions involved in visual as well as cognitive functions; these findings provide a new approach to diagnose potential brain damage in preterm infants with BPD.

Effects of local reduction of endogenous α-synuclein using antisense oligonucleotides on the fibril-induced propagation of pathology through the neural network in wild-type mice.

In Parkinson's disease and other synucleinopathies, fibrillar forms of α-synuclein (aSyn) are hypothesized to structurally convert and pathologize endogenous aSyn, which then propagates through the neural connections, forming Lewy pathologies and ultimately causing neurodegeneration. Inoculation of mouse-derived aSyn preformed fibrils (PFFs) into the unilateral striatum of wild-type mice causes widespread aSyn pathologies in the brain through the neural network. Here, we used the local injection of antisense oligonucleotides (ASOs) against Snca mRNA to confine the area of endogenous aSyn protein reduction and not to affect the PFFs properties in this model. We then varied the timing and location of ASOs injection to examine their impact on the initiation and propagation of aSyn pathologies in the whole brain and the therapeutic effect using abnormally-phosphorylated aSyn (pSyn) as an indicator. By injecting ASOs before or 0-14 days after the PFFs were inoculated into the same site in the left striatum, the reduction in endogenous aSyn in the striatum leads to the prevention and inhibition of the regional spread of pSyn pathologies to the whole brain including the contralateral right hemisphere. ASO post-injection inhibited extension from neuritic pathologies to somatic ones. Moreover, injection of ASOs into the right striatum prevented the remote regional spread of pSyn pathologies from the left striatum where PFFs were inoculated and no ASO treatment was conducted. This indicated that the reduction in endogenous aSyn protein levels at the propagation destination site can attenuate pSyn pathologies, even if those at the propagation initiation site are not inhibited, which is consistent with the original concept of prion-like propagation that endogenous aSyn is indispensable for this regional spread. Our results demonstrate the importance of recruiting endogenous aSyn in this neural network propagation model and indicate a possible potential for ASO treatment in synucleinopathies.

Atypical connectome topography and signal flow in temporal lobe epilepsy.

Temporal lobe epilepsy (TLE) is the most common pharmaco-resistant epilepsy in adults. While primarily associated with mesiotemporal pathology, recent evidence suggests that brain alterations in TLE extend beyond the paralimbic epicenter and impact macroscale function and cognitive functions, particularly memory. Using connectome-wide manifold learning and generative models of effective connectivity, we examined functional topography and directional signal flow patterns between large-scale neural circuits in TLE at rest. Studying a multisite cohort of 95 patients with TLE and 95 healthy controls, we observed atypical functional topographies in the former group, characterized by reduced differentiation between sensory and transmodal association cortices, with most marked effects in bilateral temporo-limbic and ventromedial prefrontal cortices. These findings were consistent across all study sites, present in left and right lateralized patients, and validated in a subgroup of patients with histopathological validation of mesiotemporal sclerosis and post-surgical seizure freedom. Moreover, they were replicated in an independent cohort of 30 TLE patients and 40 healthy controls. Further analyses demonstrated that reduced differentiation related to decreased functional signal flow into and out of temporolimbic cortical systems and other brain networks. Parallel analyses of structural and diffusion-weighted MRI data revealed that topographic alterations were independent of TLE-related cortical thinning but partially mediated by white matter microstructural changes that radiated away from paralimbic circuits. Finally, we found a strong association between the degree of functional alterations and behavioral markers of memory dysfunction. Our work illustrates the complex landscape of macroscale functional imbalances in TLE, which can serve as intermediate markers bridging microstructural changes and cognitive impairment.

The hippocampus as a structural and functional network epicentre for distant cortical thinning in neurocognitive aging.

Alterations in grey matter (GM) and white matter (WM) are associated with memory impairment across the neurocognitive aging spectrum and theorised to spread throughout brain networks. Functional and structural connectivity (FC,SC) may explain widespread atrophy. We tested the effect of SC and FC to the hippocampus on cortical thickness (CT) of connected areas. In 419 (223 F) participants (agemean=73 ±â€¯8) from the Alzheimer's Disease Neuroimaging Initiative, cortical regions associated with memory (Rey Auditory Verbal Learning Test) were identified using Lasso regression. Two structural equation models (SEM), for SC and resting-state FC, were fitted including CT areas, and SC and FC to the left and right hippocampus (LHIP,RHIP). LHIP (ß=-0.150,p=<.001) and RHIP (ß=-0.139,p=<.001) SC predicted left temporopolar/rhinal CT; RHIP SC predicted right temporopolar/rhinal CT (ß=-0.191,p=<.001). LHIP SC predicted right fusiform/parahippocampal (ß=-0.104,p=.011) and intraparietal sulcus/superior parietal CT (ß=0.101,p=.028). Increased RHIP FC predicted higher left inferior parietal CT (ß=0.132,p=.042) while increased LHIP FC predicted lower right fusiform/parahippocampal CT (ß=-0.97; p=.023). The hippocampi may be epicentres for cortical thinning through disrupted connectivity.

Impaired topology and connectivity of grey matter structural networks in major depressive disorder: evidence from a multi-site neuroimaging data-set.

BACKGROUND: Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD. AIMS: Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes. METHOD: A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings. RESULTS: Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms. CONCLUSIONS: Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.

Perineuronal Net Microscopy: From Brain Pathology to Artificial Intelligence.

Perineuronal nets (PNN) are a special highly structured type of extracellular matrix encapsulating synapses on large populations of CNS neurons. PNN undergo structural changes in schizophrenia, epilepsy, Alzheimer's disease, stroke, post-traumatic conditions, and some other brain disorders. The functional role of the PNN microstructure in brain pathologies has remained largely unstudied until recently. Here, we review recent research implicating PNN microstructural changes in schizophrenia and other disorders. We further concentrate on high-resolution studies of the PNN mesh units surrounding synaptic boutons to elucidate fine structural details behind the mutual functional regulation between the ECM and the synaptic terminal. We also review some updates regarding PNN as a potential pharmacological target. Artificial intelligence (AI)-based methods are now arriving as a new tool that may have the potential to grasp the brain's complexity through a wide range of organization levels-from synaptic molecular events to large scale tissue rearrangements and the whole-brain connectome function. This scope matches exactly the complex role of PNN in brain physiology and pathology processes, and the first AI-assisted PNN microscopy studies have been reported. To that end, we report here on a machine learning-assisted tool for PNN mesh contour tracing.

Abnormal longitudinal changes of structural covariance networks of cortical thickness in mild traumatic brain injury with posttraumatic headache.

BACKGROUND: It is widely acknowledged that mild traumatic brain injury (MTBI) leads to either functionally or anatomically abnormal brain regions. Structural covariance networks (SCNs) that depict coordinated regional maturation patterns are commonly employed for investigating brain structural abnormalities. However, the dynamic nature of SCNs in individuals with MTBI who suffer from posttraumatic headache (PTH) and their potential as biomarkers have hitherto not been investigated. METHODS: This study included 36 MTBI patients with PTH and 34 well-matched healthy controls (HCs). All participants underwent magnetic resonance imaging scans and were assessed with clinical measures during the acute and subacute phases. Structural covariance matrices of cortical thickness were generated for each group, and global as well as nodal network measures of SCNs were computed. RESULTS: MTBI patients with PTH demonstrated reduced headache impact and improved cognitive function from the acute to subacute phase. In terms of global network metrics, MTBI patients exhibited an abnormal normalized clustering coefficient compared to HCs during the acute phase, although no significant difference in the normalized clustering coefficient was observed between the groups during the subacute phase. Regarding nodal network metrics, MTBI patients displayed alterations in various brain regions from the acute to subacute phase, primarily concentrated in the prefrontal cortex (PFC). CONCLUSIONS: These findings indicate that the cortical thickness topography in the PFC determines the typical structural-covariance topology of the brain and may serve as an important biomarker for MTBI patients with PTH.

Selective vulnerability of the ventral hippocampus-prelimbic cortex axis parvalbumin interneuron network underlies learning deficits of fragile X mice.

High-penetrance mutations affecting mental health can involve genes ubiquitously expressed in the brain. Whether the specific patterns of dysfunctions result from ubiquitous circuit deficits or might reflect selective vulnerabilities of targetable subnetworks has remained unclear. Here, we determine how loss of ubiquitously expressed fragile X mental retardation protein (FMRP), the cause of fragile X syndrome, affects brain networks in Fmr1y/- mice. We find that in wild-type mice, area-specific knockout of FMRP in the adult mimics behavioral consequences of area-specific silencing. By contrast, the functional axis linking the ventral hippocampus (vH) to the prelimbic cortex (PreL) is selectively affected in constitutive Fmr1y/- mice. A chronic alteration in late-born parvalbumin interneuron networks across the vH-PreL axis rescued by VIP signaling specifically accounts for deficits in vH-PreL theta-band network coherence, ensemble assembly, and learning functions of Fmr1y/- mice. Therefore, vH-PreL axis function exhibits a selective vulnerability to loss of FMRP in the vH or PreL, leading to learning and memory dysfunctions in fragile X mice.

Altered cortical thickness and structural covariance networks in chronic low back pain.

BACKGROUND: Despite regional brain structural changes having been reported in patients with chronic low back pain (CLBP), the topological properties of structural covariance networks (SCNs), which refer to the organization of the SCNs, remain unclear. This study applied graph theoretical analysis to explore the alterations of the topological properties of SCNs, aiming to comprehend the integration and separation of SCNs in patients with CLBP. METHODS: A total of 38 patients with CLBP and 38 healthy controls (HCs), balanced for age and sex, were scanned using three-dimensional T1-weighted magnetic resonance imaging. The cortical thickness was extracted from 68 brain regions, according to the Desikan-Killiany atlas, and used to reconstruct the SCNs. Subsequently, graph theoretical analysis was employed to evaluate the alterations of the topological properties in the SCNs of patients with CLBP. RESULTS: In comparison to HCs, patients with CLBP had less cortical thickness in the left superior frontal cortex. Additionally, the cortical thickness of the left superior frontal cortex was negatively correlated with the Visual Analogue Scale scores of patients with CLBP. Furthermore, patients with CLBP, relative to HCs, exhibited lower global efficiency and small-worldness, as well as a longer characteristic path length. This indicates a decline in the brain's capacity to transmit and process information, potentially impacting the processing of pain signals in patients with CLBP and contributing to the development of CLBP. In contrast, there were no significant differences in the clustering coefficient, local efficiency, nodal efficiency, nodal betweenness centrality, or nodal degree between the two groups. CONCLUSIONS: From the regional cortical thickness to the complex brain network level, our study demonstrated changes in the cortical thickness and topological properties of the SCNs in patients with CLBP, thus aiding in a better understanding of the pathophysiological mechanisms of CLBP.

Localized alterations in cortical thickness and sulcal depth of the cingulo-opercular network in relation to lower reading fluency skills in children with dyslexia.

The traditional models of reading development describe how language processing and word decoding contribute to reading comprehension and how impairments in word decoding, a defining feature of dyslexia, affect reading comprehension outcomes. However, these models do not include word and sentence reading (contextual reading) fluency, both of which engage executive functions, with notably decreased performance in children with dyslexia. In the current study, we compared cortical thickness and sulcal depth (CT/SD) in the cingulo-opercular (CO) executive functions brain network in children with dyslexia and typical readers and examined associations with word vs. contextual reading fluency. Overall, CT was lower in insular regions and higher in parietal and caudal anterior cingulate cortex regions in children with dyslexia. Children with dyslexia showed positive correlations between word reading fluency and CT/SD in insular regions, whereas no significant correlations were observed in typical readers. For sentence reading fluency, negative correlations with CT/SD were found in insular regions in children with dyslexia, while positive correlations with SD were found in insular regions in typical readers. These results demonstrate the differential relations between word and sentence reading fluency and anatomical circuitry supporting executive functions in children with dyslexia vs. typical readers. It also suggests that word and sentence reading fluency, relate to morphology of executive function-related regions in children with dyslexia, whereas in typical readers, only sentence reading fluency relates to morphology of executive function regions. The results also highlight the role of the insula within the CO network in reading fluency. Here we suggest that word and sentence reading fluency are distinct components of reading that should each be included in the Simple View of Reading traditional model.

The cognitive relevance of non-lesional damage to cortical networks in people with multiple sclerosis.

BACKGROUND: Cognitive impairment, a common and debilitating symptom in people with multiple sclerosis (MS), is especially related to cortical damage. However, the impact of regional cortical damage remains poorly understood. Our aim was to evaluate structural (network) integrity in lesional and non-lesional cortex in people with MS, and its relationship with cognitive dysfunction. METHODS: In this cross-sectional study, 176 people with MS and 48 healthy controls underwent MRI, including double inversion recovery and diffusion-weighted scans, and neuropsychological assessment. Cortical integrity was assessed based on fractional anisotropy (FA) and mean diffusivity (MD) within 212 regions split into lesional or non-lesional cortex, and grouped into seven cortical networks. Integrity was compared between people with MS and controls, and across cognitive groups: cognitively-impaired (CI; ≥ two domains at Z ≤ - 2 below controls), mildly CI (≥ two at - 2 < Z ≤ - 1.5), or cognitively-preserved (CP). RESULTS: Cortical lesions were observed in 87.5% of people with MS, mainly in ventral attention network, followed by limbic and default mode networks. Compared to controls, in non-lesional cortex, MD was increased in people with MS, but mean FA did not differ. Within the same individual, MD and FA were increased in lesional compared to non-lesional cortex. CI-MS exhibited higher MD than CP-MS in non-lesional cortex of default mode, frontoparietal and sensorimotor networks, of which the default mode network could best explain cognitive performance. CONCLUSION: Diffusion differences in lesional cortex were more severe than in non-lesional cortex. However, while most people with MS had cortical lesions, diffusion differences in CI-MS were more prominent in non-lesional cortex than lesional cortex, especially within default mode, frontoparietal and sensorimotor networks.

The thalamic covariance network is associated with cognitive deficits in patients with cerebral small vascular disease.

OBJECTIVE: Abnormalities in the gray matter structure of cerebral small vessel disease (CSVD) have been observed throughout the brain. However, whether cortico-cortical connections exist between regions of gray matter atrophy in patients with CSVD has not been fully elucidated. This question was tested by comparing the gray matter covariance networks in CSVD patients with and without cognitive impairment (CI). METHODS: We performed multivariate modeling of the gray matter volume measurements of 61 patients with CI (CSVD-CI), 85 patients without CI (CSVD-NC), and 108 healthy controls using source-based morphological analysis (SBM) to obtain gray matter structural covariance networks at the population level. Then, correlations between structural covariance networks and cognitive functions were analyzed in CSVD patients. Finally, a support vector machine (SVM) classifier was used with the gray matter covariance network as a classification feature to identify CI among the CSVD population. RESULTS: The results of the analysis of all the subjects showed that compared with healthy controls, the expression of the thalamic covariance network, cerebellum covariance network, and calcarine cortex covariance network was reduced in patients with CSVD. Moreover, CSVD-CI patients showed a significant reduction in the expression of the thalamic covariance network, encompassing the thalamus and the parahippocampal gyrus, relative to CSVD-NC patients, which persisted after excluding CSVD patients with thalamic lacunes. In patients with CSVD, cognitive functions were positively correlated with measures of the thalamic covariance network. More than 80% of CSVD patients with CI were correctly identified by the SVM classifier. INTERPRETATION: Our findings provide new evidence to explain the distribution state of gray matter reduction in CSVD patients, and the thalamic covariance network is the core region for early gray matter reduction during the development of CSVD disease, which is related to cognitive deficits. Reduced expression of thalamic covariance networks may provide a neuroimaging biomarker for the early identification of cognitive impairment in CSVD patients.