Longitudinal brain changes in Parkinson's disease with severe olfactory deficit.

INTRODUCTION: Olfactory dysfunction and REM sleep behavior disorder (RBD) are associated with distinct cognitive trajectories in the course of Parkinson's disease (PD). The underlying neurobiology for this relationship remains unclear but may involve distinct patterns of neurodegeneration. This study aimed to examine longitudinal cortical atrophy and thinning in early-stage PD with severe olfactory deficit (anosmia) without and with concurrent probable RBD. METHODS: Longitudinal MRI data over four years of 134 de novo PD and 49 healthy controls (HC) from the Parkinson Progression Marker Initiative (PPMI) cohort were analyzed using a linear mixed-effects model. Patients were categorized into those with anosmia by the University of Pennsylvania Smell Identification Test (UPSIT) score ≤ 18 (AO+) and those without (UPSIT score > 18, AO-). The AO+ group was further subdivided into AO+ with probable RBD (AO+RBD+) and without (AO+RBD-) for subanalysis. RESULTS: Compared to subjects without baseline anosmia, the AO+ group exhibited greater longitudinal declines in both volume and thickness in the bilateral parahippocampal gyri and right transverse temporal gyrus. Patients with concurrent anosmia and RBD showed more extensive longitudinal declines in cortical volume and thickness, involving additional brain regions including the bilateral precuneus, left inferior temporal gyrus, right paracentral gyrus, and right precentral gyrus. CONCLUSIONS: The atrophy/thinning patterns in early-stage PD with severe olfactory dysfunction include regions that are critical for cognitive function and could provide a structural basis for previously reported associations between severe olfactory deficit and cognitive decline in PD. Concurrent RBD might enhance the dynamics of cortical changes.

Cerebellar and thalamic connector hubs facilitate the involvement of visual and cognitive networks in essential tremor.

INTRODUCTION: Connector hubs are specialized brain regions that connect multiple brain networks and therefore have the potential to affect the functions of multiple systems. This study aims to examine the involvement of connector hub regions in essential tremor. METHODS: We examined whole-brain functional connectivity alterations across multiple brain networks in 27 patients with essential tremor and 27 age- and sex-matched healthy controls to identify affected hub regions using a network metric called functional connectivity overlap ratio estimated from resting-state functional MRI. We also evaluated the relationships of affected hubs with cognitive and tremor scores in all patients and with motor function improvement scores in 15 patients who underwent postoperative follow-up evaluations after focused ultrasound thalamotomy. RESULTS: We have identified affected connector hubs in the cerebellum and thalamus. Specifically, the dentate nucleus in the cerebellum and the dorsomedial thalamus exhibited more extensive connections with the sensorimotor network in patients. Moreover, the connections of the thalamic pulvinar with the visual network were also significantly widespread in the patient group. The connections of these connector hub regions with cognitive networks were negatively associated (FDR q < 0.05) with cognitive, tremor, and motor function improvement scores. CONCLUSION: In patients with essential tremor, connector hub regions within the cerebellum and thalamus exhibited widespread functional connections with sensorimotor and visual networks, leading to alternative pathways outside the classical tremor axis. Their connections with cognitive networks also affect patients' cognitive function.

Cognitive dysfunction in de novo Parkinson disease: Remitting vs. progressive cognitive impairment.

INTRODUCTION: Parkinson's disease (PD) exhibits divergent cognitive trajectories; however, the factors contributing to these variations remain elusive. This study aimed to examine the clinical features of patients with different long-term cognitive trajectories in de novo PD over a five-year follow-up. METHODS: We analyzed 258 patients who completed every annual evaluation for five years. According to the Montreal Cognitive Assessment (MoCA) scores, we classified patients into three groups: cognitively normal (n = 118, CN), remitting MoCA decline (n = 74, RMD), and progressive MoCA decline (n = 66, PMD). RESULTS: The RMD group was associated with lower olfactory scores (Odds Ratio (OR) = 0.958, p = 0.040), whereas PMD was associated with higher depression scores (OR = 1.158, p = 0.045), probable RBD (OR = 3.169, p = 0.002), older age (OR = 1.132, p < 0.001) and lower educational attainment (OR = 0.828, p = 0.004). PMD had higher neurofilament light chain protein values than CN and RMD (p = 0.006, 0.015, respectively). Longitudinally, PMD showed a greater decline in all cognitive scores and hippocampus volumes (p = 0.004). Meanwhile, RMD exhibited intermediate cognitive and volumetric trajectories between CN and PMD and displayed worse score changes in memory tasks than CN. CONCLUSIONS: While PMD exhibited known risk factors for cognitive impairment, along with worse cognitive performance and hippocampal volume decline, RMD displayed baseline lower olfactory scores and intermediate cognitive and hippocampal volume decline between the two groups. These findings suggest individuals in RMD may still be at risk for cognitive deficits. However, further long-term follow-up data are needed to unravel the determinants and dynamics of cognitive functions.

＜Editors' Choice＞ Pattern of THK 5351 retention in normal aging involves core regions of resting state networks associated with higher cognitive function.

We aimed to elucidate the distribution pattern of the positron emission tomography probe [18F]THK 5351, a marker for astrogliosis and tau accumulation, in healthy aging. We also assessed the relationship between THK5351 retention and resting state networks. We enrolled 62 healthy participants in this study. All participants underwent magnetic resonance imaging/positron emission tomography scanning consisting of T1-weighted images, resting state functional magnetic resonance imaging, Pittsburgh Compound-B and THK positron emission tomography. The preprocessed THK images were entered into a scaled subprofile modeling/principal component analysis to extract THK distribution patterns. Using the most significant THK pattern, we generated regions of interest, and performed seed-based functional connectivity analyses. We also evaluated the functional connectivity overlap ratio to identify regions with high between-network connectivity. The most significant THK distributions were observed in the medial prefrontal cortex and bilateral putamen. The seed regions of interest in the medial prefrontal cortex had a functional connectivity map that significantly overlapped with regions of the dorsal default mode network. The seed regions of interest in the putamen showed strong overlap with the basal ganglia and anterior salience networks. The functional connectivity overlap ratio also showed that three peak regions had the characteristics of connector hubs. We have identified an age-related spatial distribution of THK in the medial prefrontal cortex and basal ganglia in normal aging. Interestingly, the distribution's peaks are located in regions of connector hubs that are strongly connected to large-scale resting state networks associated with higher cognitive function.

Magnetic resonance-guided focused ultrasound thalamotomy restored distinctive resting-state networks in patients with essential tremor.

OBJECTIVE: Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy ameliorates symptoms in patients with essential tremor (ET). How this treatment affects canonical brain networks has not been elucidated. The purpose of this study was to clarify changes of brain networks after MRgFUS thalamotomy in ET patients by analyzing resting-state networks (RSNs). METHODS: Fifteen patients with ET were included in this study. Left MRgFUS thalamotomy was performed in all cases, and MR images, including resting-state functional MRI (rsfMRI), were taken before and after surgery. MR images of 15 age- and sex-matched healthy controls (HCs) were also used for analysis. Using rsfMRI data, canonical RSNs were extracted by performing dual regression analysis, and the functional connectivity (FC) within respective networks was compared among pre-MRgFUS patients, post-MRgFUS patients, and HCs. The severity of tremor was evaluated using the Clinical Rating Scale for Tremor (CRST) score pre- and postoperatively, and its correlation with RSNs was examined. RESULTS: Preoperatively, ET patients showed a significant decrease in FC in the sensorimotor network (SMN), primary visual network (VN), and visuospatial network (VSN) compared with HCs. The decrease in FC in the SMN correlated with the severity of tremor. After MRgFUS thalamotomy, ET patients still exhibited a significant decrease in FC in a small area of the SMN, but they exhibited an increase in the cerebellar network (CN). In comparison between pre- and post-MRgFUS patients, the FC in the SMN and the VSN significantly increased after treatment. Quantitative evaluation of the FCs in these three groups showed that the SMN and VSN increased postoperatively and demonstrated a trend toward those of HCs. CONCLUSIONS: The SMN and CN, which are considered to be associated with the cerebello-thalamo-cortical loop, exhibited increased connectivity after MRgFUS thalamotomy. In addition, the FC of the visual network, which declined in ET patients compared with HCs, tended to normalize postoperatively. This could be related to the hypothesis that visual feedback is involved in tremor severity in ET patients. Overall, the analysis of the RSNs by rsfMRI reflected the pathophysiology with the intervention of MRgFUS thalamotomy in ET patients and demonstrated a possibility of a biomarker for successful treatment.

Connectivity impairment of cerebellar and sensorimotor connector hubs in Parkinson's disease.

Cognitive and movement processes involved integration of several large-scale brain networks. Central to these integrative processes are connector hubs, brain regions characterized by strong connections with multiple networks. Growing evidence suggests that many neurodegenerative and psychiatric disorders are associated with connector hub dysfunctions. Using a network metric called functional connectivity overlap ratio, we investigated connector hub alterations in Parkinson's disease. Resting-state functional MRI data from 99 patients (male/female = 44/55) and 99 age- and sex-matched healthy controls (male/female = 39/60) participating in our cross-sectional study were used in the analysis. We have identified two sets of connector hubs, mainly located in the sensorimotor cortex and cerebellum, with significant connectivity alterations with multiple resting-state networks. Sensorimotor connector hubs have impaired connections primarily with primary processing (sensorimotor, visual), visuospatial, and basal ganglia networks, whereas cerebellar connector hubs have impaired connections with basal ganglia and executive control networks. These connectivity alterations correlated with patients' motor symptoms. Specifically, values of the functional connectivity overlap ratio of the cerebellar connector hubs were associated with tremor score, whereas that of the sensorimotor connector hubs with postural instability and gait disturbance score, suggesting potential association of each set of connector hubs with the disorder's two predominant forms, the akinesia/rigidity and resting tremor subtypes. In addition, values of the functional connectivity overlap ratio of the sensorimotor connector hubs were highly predictive in classifying patients from controls with an accuracy of 75.76%. These findings suggest that, together with the basal ganglia, cerebellar and sensorimotor connector hubs are significantly involved in Parkinson's disease with their connectivity dysfunction potentially driving the clinical manifestations typically observed in this disorder.

Involvement of cerebellar and subcortical connector hubs in schizophrenia.

BACKGROUND: Schizophrenia is considered a brain connectivity disorder in which functional integration within the brain fails. Central to the brain's integrative function are connector hubs, brain regions characterized by strong connections with multiple networks. Given their critical role in functional integration, we hypothesized that connector hubs, including those located in the cerebellum and subcortical regions, are severely impaired in patients with schizophrenia. METHODS: We identified brain voxels with significant connectivity alterations in patients with schizophrenia (n = 76; men = 43) compared to healthy controls (n = 80; men = 43) across multiple large-scale resting state networks (RSNs) using a network metric called functional connectivity overlap ratio (FCOR). From these voxels, candidate connector hubs were identified and verified using seed-based connectivity analysis. RESULTS: We found that most networks exhibited connectivity alterations in the patient group. Specifically, connectivity with the basal ganglia and high visual networks was severely affected over widespread brain areas in patients, affecting subcortical and cerebellar regions and the regions involved in visual and sensorimotor processing. Furthermore, we identified critical connector hubs in the cerebellum, midbrain, thalamus, insula, and calcarine with connectivity to multiple RSNs affected in the patients. FCOR values of these regions were also associated with clinical data and could classify patient and control groups with > 80 % accuracy. CONCLUSIONS: These findings highlight the critical role of connector hubs, particularly those in the cerebellum and subcortical regions, in the pathophysiology of schizophrenia and the potential role of FCOR as a clinical biomarker for the disorder.

Reproducibility of functional connectivity metrics estimated from resting-state functional MRI with differences in days, coils, and global signal regression.

In multisite studies, differences in imaging acquisition systems could affect the reproducibility of the results when examining changes in brain function using resting-state functional magnetic resonance imaging (rs-fMRI). This is also important for longitudinal studies, in which changes in equipment settings can occur. This study examined the reproducibility of functional connectivity (FC) metrics estimated from rs-fMRI data acquired using scanner receiver coils with different numbers of channels. This study involved 80 rs-fMRI datasets from 20 healthy volunteers scanned in two independent imaging sessions using both 12- and 32-channel coils for each session. We used independent component analysis (ICA) to evaluate the FC of canonical resting-state networks (RSNs) and graph theory to calculate several whole-brain network metrics. The effect of global signal regression (GSR) as a preprocessing step was also considered. Comparisons within and between receiver coils were performed. Irrespective of the GSR, RSNs derived from rs-fMRI data acquired using the same receiver coil were reproducible, but not from different receiver coils. However, both the GSR and the channel count of the receiver coil have discernible effects on the reproducibility of network metrics estimated using whole-brain network analysis. The data acquired using the 32-channel coil tended to have better reproducibility than those acquired using the 12-channel coil. Our findings suggest that the reproducibility of FC metrics estimated from rs-fMRI data acquired using different receiver coils showed some level of dependence on the preprocessing method and the type of analysis performed.

Functional connector hubs in the cerebellum.

Accumulating evidence from anatomical and neuroimaging studies suggests that the cerebellum is engaged in a variety of motor and cognitive tasks. Given its various functions, a key question is whether the cerebellum also plays an important role in the brain's integrative functions. Here, we hypothesize the existence of connector regions, also known as connector hubs, where multiple resting state networks converged in the cerebellum. To verify this, we employed a recently developed voxel-level network measure called functional connectivity overlap ratio (FCOR), which could be used to quantify the spatial extent of a region's connection to several large-scale cortical networks. Using resting state functional MRI data from 101 healthy participants, cerebellar FCOR maps were constructed and used to identify the locations of connector hubs in the cerebellum. Results showed that a number of cerebellar regions exhibited strong connectivity with multiple functional networks, verifying our hypothesis. These highly connected regions were located in the posterior cerebellum, especially in lobules VI, VII, and IX, and mainly connected to the core neurocognitive networks such as default mode and executive control networks. Regions associated with the sensorimotor network were also localized in lobule V, VI, and VIII, albeit in small clusters. These cerebellar connector hubs may play an essential role in the processing of information across the core neurocognitive networks.

Reserve and Maintenance in the Aging Brain: A Longitudinal Study of Healthy Older Adults.

The aging brain undergoes structural changes even in very healthy individuals. Quantifying these changes could help disentangle pathologic changes from those associated with the normal human aging process. Using longitudinal magnetic resonance imaging (MRI) data from 227 carefully selected healthy human cohort with age ranging from 50 to 80 years old at baseline scan, we quantified age-related volumetric changes in the brain of healthy human older adults. Longitudinally, the rates of tissue loss in total gray matter (GM) and white matter (WM) were 2497.5 and 2579.8 mm3 per year, respectively. Across the whole brain, the rates of GM decline varied with regions in the frontal and parietal lobes having faster rates of decline, whereas some regions in the occipital and temporal lobes appeared relatively preserved. In contrast, cross-sectional changes were mainly observed in the temporal-occipital regions. Similar longitudinal atrophic changes were also observed in subcortical regions including thalamus, hippocampus, putamen, and caudate, whereas the pallidum showed an increasing volume with age. Overall, regions maturing late in development (frontal, parietal) are more vulnerable to longitudinal decline, whereas those that fully mature in the early stage (temporal, occipital) are mainly affected by cross-sectional changes in healthy older cohort. This may suggest that, for a successful healthy aging, the former needs to be maximally developed at an earlier age to compensate for the longitudinal decline later in life and the latter to remain relatively preserved even in old age, consistent with both concepts of reserve and brain maintenance.

Fiber-specific white matter analysis reflects upper motor neuron impairment in amyotrophic lateral sclerosis.

BACKGROUND AND PURPOSE: To clarify the relationship between fiber-specific white matter changes in amyotrophic lateral sclerosis (ALS) and clinical signs of upper motor neuron (UMN) involvement, we performed a fixel-based analysis (FBA), a novel framework for diffusion-weighted imaging analysis. METHODS: We enrolled 96 participants, including 48 nonfamilial ALS patients and 48 age- and sex-matched healthy controls (HCs), in this study and conducted whole-brain FBA and voxel-based morphometry analysis. We compared the fiber density (FD), fiber morphology (fiber cross-section [FC]), and a combined index of FD and FC (FDC) between the ALS and HC groups. We performed a tract-of-interest analysis to extract FD values across the significant regions in the whole-brain analysis. Then, we evaluated the associations between FD values and clinical variables. RESULTS: The bilateral corticospinal tracts (CSTs) and the corpus callosum (CC) showed reduced FD and FDC in ALS patients compared with HCs (p < 0.05, familywise error-corrected), and the comparison of FCs revealed no region that was significantly different from another. Voxel-based morphometry showed cortical volume reduction in the regions, including the primary motor area. Clinical scores showed correlations with FD values in the CSTs (UMN score: rho = -0.530, p < 0.001; central motor conduction time [CMCT] in the upper limb: rho = -0.474, p = 0.008; disease duration: rho = -0.383, p = 0.007; ALS Functional Rating Scale-Revised: rho = 0.340, p = 0.018). In addition, patients whose CMCT was not calculated due to unevoked waves also showed FD reduction in the CSTs. CONCLUSIONS: Our findings suggest that FD values in the CST estimated via FBA can be potentially used in evaluating UMN impairments.

Involvement of brain structures in childhood epilepsy with centrotemporal spikes.

BACKGROUND: We aimed to investigate electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) findings to elucidate the interictal epileptiform discharge (IED)-related functional alterations in deep brain structures and the neocortex in childhood epilepsy with centrotemporal spikes (CECTS). METHODS: Ten children with CECTS (median age 8.2 years), referred to our hospital within a year of onset, were eligible for inclusion. They underwent EEG-fMRI recording during sleep. Llongitudinal evaluations, including medical examinations, intelligence tests, and questionnaires about developmental disabilities, were performed. The initial evaluation was performed at the same time as the EEG-fMRI, and the second evaluation was performed over 2 years after the initial evaluation. RESULTS: Three children were unable to maintain sleep during the EEG-fMRI recording, and the remaining seven children were eligible for further assessment. All patients showed unilateral-dominant centrotemporal spikes during scans. One patient had only positive hemodynamic responses, while the others had both positive and negative hemodynamic responses. All patients showed IED-related hemodynamic responses in the bilateral neocortex. For deep brain structures, IED-related hemodynamic responses were observed in the cingulate gyrus (n = 4), basal ganglia (n = 3), thalamus (n = 2), and default mode network (n = 1). Seizure frequencies at the second evaluation were infrequent or absent, and the longitudinal results of intelligence tests and questionnaires were within normal ranges. CONCLUSIONS: We demonstrated that IEDs affect broad brain areas, including deep brain structures such as the cingulate gyrus, basal ganglia, and thalamus. Deep brain structures may play an important role in the pathophysiology of CECTS.

Multitier Network Analysis Using Resting-state Functional MRI for Epilepsy Surgery.

Resting-state functional MRI (rs-fMRI) has been utilized to visualize large-scale brain networks. We evaluated the usefulness of multitier network analysis using rs-fMRI in patients with focal epilepsy. Structural and rs-fMRI data were retrospectively evaluated in 20 cases with medically refractory focal epilepsy, who subsequently underwent surgery. First, structural changes were examined using voxel-based morphometry analysis. Second, alterations in large-scale networks were evaluated using dual-regression analysis. Third, changes in cortical hubs were analyzed and the relationship between aberrant hubs and the epileptogenic zone (EZ) was evaluated. Finally, the relationship between the hubs and the default mode network (DMN) was examined using spectral dynamic causal modeling (spDCM). Dual-regression analysis revealed significant decrease in functional connectivity in several networks including DMN in patients, although no structural difference was seen between groups. Aberrant cortical hubs were observed in and around the EZ (EZ hubs) in 85% of the patients, and a strong degree of EZ hubs correlated to good seizure outcomes postoperatively. In spDCM analysis, facilitation was often seen from the EZ hub to the contralateral side, while inhibition was seen from the EZ hub to nodes of the DMN. Some cognition-related networks were impaired in patients with focal epilepsy. The EZ hub appeared in the vicinity of EZ facilitating connections to distant regions in the early phase, which may eventually generate secondary focus, while inhibiting connections to the DMN, which may cause cognitive deterioration. Our results demonstrate pathological network alterations in epilepsy and suggest that earlier surgical intervention may be more effective.

Characteristics of Neural Network Changes in Normal Aging and Early Dementia.

To understand the mechanisms underlying preserved and impaired cognitive function in healthy aging and dementia, respectively, the spatial relationships of brain networks and mechanisms of their resilience should be understood. The hub regions of the brain, such as the multisensory integration and default mode networks, are critical for within- and between-network communication, remain well-preserved during aging, and play an essential role in compensatory processes. On the other hand, these brain hubs are the preferred sites for lesions in neurodegenerative dementias, such as Alzheimer's disease. Disrupted primary information processing networks, such as the auditory, visual, and sensorimotor networks, may lead to overactivity of the multisensory integration networks and accumulation of pathological proteins that cause dementia. At the cellular level, the brain hub regions contain many synapses and require a large amount of energy. These regions are rich in ATP-related gene expression and had high glucose metabolism as demonstrated on positron emission tomography (PET). Importantly, the number and function of mitochondria, which are the center of ATP production, decline by about 8% every 10 years. Dementia patients often have dysfunction of the ubiquitin-proteasome and autophagy-lysosome systems, which require large amounts of ATP. If there is low energy supply but the demand is high, the risk of disease can be high. Imbalance between energy supply and demand may cause accumulation of pathological proteins and play an important role in the development of dementia. This energy imbalance may explain why brain hub regions are vulnerable to damage in different dementias. Here, we review (1) the characteristics of gray matter network, white matter network, and resting state functional network changes related to resilience in healthy aging, (2) the mode of resting state functional network disruption in neurodegenerative dementia, and (3) the cellular mechanisms associated with the disruption.

Resting State Networks Related to the Maintenance of Good Cognitive Performance During Healthy Aging.

Purpose: Maintenance of cognitive performance is important for healthy aging. This study aims to elucidate the relationship between brain networks and cognitive function in subjects maintaining relatively good cognitive performance. Methods: A total of 120 subjects, with equal number of participants from each age group between 20 and 70 years, were included in this study. Only participants with Addenbrooke's Cognitive Examination - Revised (ACE-R) total score greater than 83 were included. Anatomical T1-weighted MR images and resting-state functional MR images (rsfMRIs) were taken from all participants using a 3-tesla MRI scanner. After preprocessing, several factors associated with age including the ACE-R total score, scores of five domains, sub-scores of ACE-R, and brain volumes were tested. Morphometric changes associated with age were analyzed using voxel based morphometry (VBM) and changes in resting state networks (RSNs) were examined using dual regression analysis. Results: Significant negative correlations with age were seen in the total gray matter volume (GMV, r = -0.58), and in the memory, attention, and visuospatial domains. Among the different sub-scores, the score of the delayed recall (DR) showed the highest negative correlation with age (r = -0.55, p < 0.001). In VBM analysis, widespread regions demonstrated negative correlation with age, but none with any of the cognitive scores. Quadratic approximations of cognitive scores as functions of age showed relatively delayed decline compared to total GMV loss. In dual regression analysis, some cognitive networks, including the dorsal default mode network, the lateral dorsal attention network, the right / left executive control network, the posterior salience network, and the language network, did not demonstrate negative correlation with age. Some regions in the sensorimotor networks showed positive correlation with the DR, memory, and fluency scores. Conclusion: Some domains of the cognitive test did not correlate with age, and even the highly correlated sub-scores such as the DR score, showed delayed decline compared to the loss of total GMV. Some RSNs, especially involving cognitive control regions, were relatively maintained with age. Furthermore, the scores of memory, fluency, and the DR were correlated with the within-network functional connectivity values of the sensorimotor network, which supported the importance of exercise for maintenance of cognition.

Bridging large-scale cortical networks: Integrative and function-specific hubs in the thalamus.

The thalamus is critical for the brain's integrative hub functions; however, the localization and characterization of the different thalamic hubs remain unclear. Using a voxel-level network measure called functional connectivity overlap ratio (FCOR), we examined the thalamus' association with large-scale resting-state networks (RSNs) to elucidate its connector hub roles. Connections to the core-neurocognitive networks were localized in the anterior and medial parts, such as the anteroventral and mediodorsal nuclei areas. Regions functionally connected to the sensorimotor network were distinctively located around the lateral pulvinar nucleus but to a limited extent. Prominent connector hubs include the anteroventral, ventral lateral, and mediodorsal nuclei with functional connections to multiple RSNs. These findings suggest that the thalamus, with extensive connections to most of the RSNs, is well placed as a critical integrative functional hub and could play an important role for functional integration facilitating brain functions associated with primary processing and higher cognition.

Effects of Head Motion on the Evaluation of Age-related Brain Network Changes Using Resting State Functional MRI.

PURPOSE: The estimation of functional connectivity (FC) measures using resting state functional MRI (fMRI) is often affected by head motion during functional imaging scans. Head motion is more common in the elderly than in young participants and could therefore affect the evaluation of age-related changes in brain networks. Thus, this study aimed to investigate the influence of head motion in FC estimation when evaluating age-related changes in brain networks. METHODS: This study involved 132 healthy volunteers divided into 3 groups: elderly participants with high motion (OldHM, mean age (±SD) = 69.6 (±5.31), N = 44), elderly participants with low motion (OldLM, mean age (±SD) = 68.7 (±4.59), N = 43), and young adult participants with low motion (YugLM, mean age (±SD) = 27.6 (±5.26), N = 45). Head motion was quantified using the mean of the framewise displacement of resting state fMRI data. After preprocessing all resting state fMRI datasets, several resting state networks (RSNs) were extracted using independent component analysis (ICA). In addition, several network metrics were also calculated using network analysis. These FC measures were then compared among the 3 groups. RESULTS: In ICA, the number of voxels with significant differences in RSNs was higher in YugLM vs. OldLM comparison than in YugLM vs. OldHM. In network analysis, all network metrics showed significant (P < 0.05) differences in comparisons involving low vs. high motion groups (OldHM vs. OldLM and OldHM vs. YugLM). However, there was no significant (P > 0.05) difference in the comparison involving the low motion groups (OldLM vs. YugLM). CONCLUSION: Our findings showed that head motion during functional imaging could significantly affect the evaluation of age-related brain network changes using resting state fMRI data.

Aging Impacts the Overall Connectivity Strength of Regions Critical for Information Transfer Among Brain Networks.

Recent studies have demonstrated that connector hubs, regions considered critical for the flow of information across neural systems, are mostly involved in neurodegenerative dementia. Considering that aging can significantly affect the brain's intrinsic connectivity, identifying aging's impact on these regions' overall connection strength is important to differentiate changes associated with healthy aging from neurodegenerative disorders. Using resting state functional magnetic resonance imaging data from a carefully selected cohort of 175 healthy volunteers aging from 21 to 86 years old, we computed an intrinsic connectivity contrast (ICC) metric, which quantifies a region's overall connectivity strength, for whole brain, short-range, and long-range connections and examined age-related changes of this metric over the adult lifespan. We have identified a limited number of hub regions with ICC values that showed significant negative relationship with age. These include the medial precentral/midcingulate gyri and insula with both their short-range and long-range (and thus whole-brain) ICC values negatively associated with age, and the angular, middle frontal, and posterior cingulate gyri with their long-range ICC values mainly involved. Seed-based connectivity analyses further confirmed that these regions are connector hubs with connectivity profile that strongly overlapped with multiple large-scale brain networks. General cognitive performance was not associated with these hubs' ICC values. These findings suggest that even healthy aging could negatively impact the efficiency of regions critical for facilitating information transfer among different functional brain networks. The extent of the regions involved, however, was limited.

Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites.

Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysiology of its alterations remains unclear. Multivariate pattern recognition analysis such as support vector machines can classify patients and healthy controls by detecting subtle and spatially distributed patterns of structural alterations. We aimed to use a support vector machine to distinguish patients with schizophrenia from control participants on the basis of structural magnetic resonance imaging data and delineate the patterns of structural alterations that significantly contributed to the classification performance. We used independent datasets from different sites with different magnetic resonance imaging scanners, protocols and clinical characteristics of the patient group to achieve a more accurate estimate of the classification performance of support vector machines. We developed a support vector machine classifier using the dataset from one site (101 participants) and evaluated the performance of the trained support vector machine using a dataset from the other site (97 participants) and vice versa. We assessed the performance of the trained support vector machines in each support vector machine classifier. Both support vector machine classifiers attained a classification accuracy of >70% with two independent datasets indicating a consistently high performance of support vector machines even when used to classify data from different sites, scanners and different acquisition protocols. The regions contributing to the classification accuracy included the bilateral medial frontal cortex, superior temporal cortex, insula, occipital cortex, cerebellum, and thalamus, which have been reported to be related to the pathogenesis of schizophrenia. These results indicated that the support vector machine could detect subtle structural brain alterations and might aid our understanding of the pathophysiology of these changes in schizophrenia, which could be one of the diagnostic findings of schizophrenia.

The neural network basis of altered decision-making in patients with amyotrophic lateral sclerosis.

OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is a multisystem disorder associated with motor impairment and behavioral/cognitive involvement. We examined decision-making features and changes in the neural hub network in patients with ALS using a probabilistic reversal learning task and resting-state network analysis, respectively. METHODS: Ninety ALS patients and 127 cognitively normal participants performed this task. Data from 62 ALS patients and 63 control participants were fitted to a Q-learning model. RESULTS: ALS patients had anomalous decision-making features with little shift in choice until they thought the value of the two alternatives had become equal. The quantified parameters (Pαß) calculated by logistic regression analysis with learning rate and inverse temperature well represented the unique choice pattern of ALS patients. Resting-state network analysis demonstrated a strong correlation between Pαß and decreased degree centrality in the anterior cingulate gyrus and frontal pole. INTERPRETATION: Altered decision-making in ALS patients may be related to the decreased hub function of medial prefrontal areas.