An unbiased data-driven age-related structural brain parcellation for the identification of intrinsic brain volume changes over the adult lifespan

Frequency-specific network changes in mesial temporal lobe epilepsy: Analysis of chronic and transient dysfunctions in the temporo-amygdala-orbitofrontal network using magnetoencephalography

OBJECTIVE

Mesial temporal lobe epilepsy (MTLE) is associated with disruptions in the temporo-amygdala-orbitofrontal (TAO) network, a key component of the limbic system. We aimed to investigate TAO network alterations in patients with MTLE using magnetoencephalography (MEG), which overcomes susceptibility artifacts that limit functional MRI analysis of the orbitofrontal cortex.

METHODS

Nine seizure-free patients with MTLE post-temporal lobectomy and nine age- and sex-matched healthy controls were recruited. Preoperative MEG data were collected and segmented into frequency bands ranging from delta to ripple to assess functional connectivity (FC) between the bilateral hippocampi and TAO network.

RESULTS

Patients with MTLE exhibited increased FC between the affected hippocampus and amygdala across all frequency bands. Additionally, FC between the affected hippocampus and the medial prefrontal cortex (mPFC), orbitofrontal gyrus (OFG), and amygdala was elevated in the gamma and ripple bands compared with healthy controls. Conversely, FC between the healthy hippocampus and mPFC decreased in the alpha and beta bands. Furthermore, FC within the TAO network fluctuated before and after epileptic spikes; there was a decrease in the delta band between the bilateral hippocampi and the amygdala, OFG, and thalamus, whereas FC between the hippocampus and mPFC increased in the alpha, beta, and ripple bands.

SIGNIFICANCE

These findings suggest the formation of an abnormal network involving the affected hippocampus and the TAO network, particularly in the gamma-ripple bands, indicating epilepsy-induced network disruptions. Reduced FC in the healthy hippocampus and the TAO network may reflect frontal lobe dysfunction related to emotion and cognition. Additionally, both chronic and transient FC changes observed via MEG may contribute to the cognitive and psychiatric impairments experienced by patients with MTLE. This study highlights the significance of frequency-specific network alterations in understanding MTLE's pathophysiology and its impact on limbic system functions.

PLAIN LANGUAGE SUMMARY

In mesial temporal lobe epilepsy, there may be abnormal connectivity between the hippocampus and the limbic system, which is involved in memory, cognition, and emotion. The changes in connectivity observed using magnetoencephalography may be implicated in cognitive and psychiatric problems experienced by patients with mesial temporal lobe epilepsy. Examining disruptions in the connectivity across brain regions in relation to epileptic activity could further the understanding of the pathophysiology of this debilitating condition and its impact on behavioral and emotional functions, among others.

The lifelong nonlinear development of spatial variability of brain signals

The physiological information carried by brain signals is distinguished by their mean and variability. Research has indicated that both the variability of local signals and the spatial mean of the whole-brain signal (known as the global signal, GS) are sensitive to brain development. This raises the question of whether the spatial variability of the whole-brain signal, referred to as global variability (GV), could potentially serve as a more specific marker of brain development. We first established the reliability of GV and its topography (GVtopo) using data from the Human Connectome Project (HCP). Then, we examined the age-related patterns of GV and GVtopo in the Nathan Kline Institute Rockland Sample (NKI-RS; N = 968, ages ranging from 6 to 85 years) and validated these findings in an independent dataset from Southwest University (SALD; N = 492, ages ranging from 19 to 80 years). Our results demonstrated the robustness of GV and GVtopo, with intra-class correlation coefficients surpassing 0.61. Both GV and GVtopo exhibited distinct non-linear developmental trajectories, differring from those of GS and its topography. Furthermore, GV demonstrated substantial age-predictive capability, underscoring its potential as a valuable marker of brain development and its significance for future age-related research.

Mapping and modeling age-related changes in intrinsic neural timescales

Intrinsic timescales of brain regions exhibit heterogeneity, escalating with hierarchical levels, and are crucial for the temporal integration of external stimuli. Aging, often associated with cognitive decline, involves progressive neuronal and synaptic loss, reshaping brain structure and dynamics. However, the impact of these structural changes on temporal coding in the aging brain remains unclear. We mapped intrinsic timescales and gray matter volume (GMV) using magnetic resonance imaging (MRI) in young and elderly adults. We found shorter intrinsic timescales across multiple large-scale functional networks in the elderly cohort, and a significant positive association between intrinsic timescales and GMV. Additionally, age-related decline in performance on visual discrimination tasks was linked to a reduction in intrinsic timescales in the cuneus. To explain these age-related shifts, we developed an age-dependent spiking neuron network model. In younger subjects, brain regions were near a critical branching regime, while regions in elderly subjects had fewer neurons and synapses, pushing the dynamics toward a subcritical regime. The model accurately reproduced the empirical results, showing longer intrinsic timescales in young adults due to critical slowing down. Our findings reveal how age-related structural brain changes may drive alterations in brain dynamics, offering testable predictions and informing possible interventions targeting cognitive decline.

Combined quantitative analysis of the nigro-striata system in multiple system atrophy and Parkinson's disease

Degeneration of the nigrostriatal system occurs in multiple system atrophy (MSA) and Parkinson's disease (PD) via distinct pathological mechanisms. Here, we investigated nigrostriatal degeneration in MSA and PD by combining a newly developed method for evaluating the regional accumulation of dopamine transporter single-photon emission computed tomography (DAT SPECT) and individual voxel-based morphometry adjusting covariates (iVAC). We recruited 17 MSA patients and 13 PD patients, and compared their clinical and imaging indices. All patients underwent DAT SPECT and head three-dimensional T1-weighted magnetic resonance imaging. We calculated the specific binding ratio (SBR) of the putamen and caudate separately using a region-based method, and evaluated the association between the SBR and iVAC Z-score. SBR values of the putamen and caudate were lower in the PD group than in the MSA group (p < 0.001 for both). The MSA and PD groups had lower SBR values in the putamen than in the caudate (p < 0.05 and p < 0.001, respectively). We found a negative correlation between the putamen SBR and iVAC Z-score in MSA (p < 0.001, r = -0.775), but such a correlation was not detected in PD. For the caudate, there was no correlation between the SBR and iVAC Z-score in MSA and PD. Our results indicate different mechanisms of reduced uptake of DATs in MSA and PD. The association between the striatal SBR and iVAC Z-score suggests parallel degeneration in the substantia nigra and striatum in MSA.

High Blood Pressure and Impaired Brain Health: Investigating the Neuroprotective Potential of Magnesium

High blood pressure (BP) is a significant contributor to the disease burden globally and is emerging as an important cause of morbidity and mortality in the young as well as the old. The well-established impact of high BP on neurodegeneration, cognitive impairment, and dementia is widely acknowledged. However, the influence of BP across its full range remains unclear. This review aims to explore in more detail the effects of BP levels on neurodegeneration, cognitive function, and dementia. Moreover, given the pressing need to identify strategies to reduce BP levels, particular attention is placed on reviewing the role of magnesium (Mg) in ageing and its capacity to lower BP levels, and therefore potentially promote brain health. Overall, the review aims to provide a comprehensive synthesis of the evidence linking BP, Mg and brain health. It is hoped that these insights will inform the development of cost-effective and scalable interventions to protect brain health in the ageing population.

Brain age of rhesus macaques over the lifespan

Through the application of machine learning algorithms to neuroimaging data the brain age methodology was shown to provide a useful individual-level biological age prediction and identify key brain regions responsible for the prediction. In this study, we present the methodology of constructing a rhesus macaque brain age model using a machine learning algorithm and discuss the key predictive brain regions in comparison to the human brain, to shed light on cross-species primate similarities and differences. Structural information of the brain (e.g., parcellated volumes) from brain magnetic resonance imaging of 43 rhesus macaques were used to develop brain atlas-based features to build a brain age model that predicts biological age. The best-performing model used 22 selected features and achieved an R2 of 0.72. We also identified interpretable predictive brain features including Right Fronto-orbital Cortex, Right Frontal Pole, Right Inferior Lateral Parietal Cortex, and Bilateral Posterior Central Operculum. Our findings provide converging evidence of the parallel and comparable brain regions responsible for both non-human primates and human biological age prediction.

Tracking Age-Related Topological Changes in Individual Brain Morphological Networks Across the Human Lifespan

BACKGROUND

Many studies have shown topological alterations associated with age in population-based brain morphological networks. However, it is not clear how individual brain morphological networks change with age across the lifespan.

PURPOSE

To characterize age-related topological changes in individual networks and investigate the relationships between individual- and group-based brain networks at the nodal, modular, and connectome levels.

STUDY TYPE

Retrospective analysis.

POPULATION

One hundred seventy-nine healthy subjects (108 males and 71 females), aged 6-85 years with a median age of 32 years and an inter-quartile range (IQR) of 26 years.

FIELD STRENGTH/SEQUENCE

T1-weighted images using the magnetization-prepared rapid gradient echo (MPRAGE) sequences.

ASSESSMENT

Two nodal-level indicators (nodal similarity and node matching), five modular-level indicators (modularity, intra/inter-module similarity, adjusted mutual information [AMI], and module variation), and five connectome-level indicators (global efficiency, characteristic path length, clustering coefficient, local efficiency, and individual contribution) were calculated in brain morphological networks. Regression models for different indicators were built to examine their lifetime trajectory patterns.

STATISTICAL TESTS

Single-sample t-test, Mantel's test, Pearson correlation coefficient. A P value <0.05 was considered statistically significant.

RESULTS

Among 68 nodes, 34 nodes showed significant age-related patterns (all P < 0.05, FDR-corrected) in nodal similarity, including linear decline and quadratic trends. The lifespan trajectory of the connectome-level topological attributes of the individual networks presented U-shaped or inverse U-shaped trends with age. Between the individual- and group-based brain networks, the average nodal similarity was 0.67 and the average AMI of module partitions was 0.57.

DATA CONCLUSION

The lifespan trajectories of the nodal similarity mainly followed linear decreasing and nonlinear trends, whereas the modularity and the global topological attributes exhibited nonlinear patterns. There was a high degree of consistency at both nodal similarity and modular division between the individual and group networks.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 1.

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.

Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders

Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer's disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.

Connectivity alteration in thalamic nuclei and default mode network-related area in memory processes in mesial temporal lobe epilepsy using magnetoencephalography

This work aimed to investigate the involvement of the thalamic nuclei in mesial temporal lobe epilepsy (MTLE) and identify the influence of interictal epileptic discharges on the neural basis of memory processing by evaluating the functional connectivity (FC) between the thalamic nuclei and default mode network-related area (DMNRA) using magnetoencephalography. Preoperative datasets of nine patients with MTLE with seizure-free status after surgery and those of nine healthy controls were analyzed. The FC between the thalamic nuclei (anterior nucleus [ANT], mediodorsal nucleus [MD], intralaminar nuclei [IL]), hippocampus, and DMNRA was examined for each of the resting, pre-spike, spike, and post-spike periods in the delta to ripple bands using magnetoencephalography. The FC between the ANT, MD, hippocampus, and medial prefrontal cortex increased in the gamma to ripple bands, whereas the FC between the ANT, IL, and DMNRA decreased in the delta to beta bands, compared with that of the healthy controls at rest. Compared with the rest period, the pre-spike period had significantly decreased FC between the ANT, MD, and DMNRA in the ripple band. Different FC changes between the thalamic nuclei, hippocampus, and DMNRA of specific connections in a particular band may reflect impairment or compensation in the memory processes.

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.

Intergenerational concordance of brain structure between depressed mothers and their never-depressed daughters

AIM

Parents have significant genetic and environmental influences, which are known as intergenerational effects, on the cognition, behavior, and brain of their offspring. These intergenerational effects are observed in patients with mood disorders, with a particularly strong association of depression between mothers and daughters. The main purpose of our study was to investigate female-specific intergenerational transmission patterns in the human brain among patients with depression and their never-depressed offspring.

METHODS

We recruited 78 participants from 34 families, which included remitted parents with a history of depression and their never-depressed biological offspring. We used source-based and surface-based morphometry analyses of magnetic resonance imaging data to examine the degree of associations in brain structure between four types of parent-offspring dyads (i.e. mother-daughter, mother-son, father-daughter, and father-son).

RESULTS

Using independent component analysis, we found a significant positive correlation of gray matter structure between exclusively the mother-daughter dyads within brain regions located in the default mode and central executive networks, such as the bilateral anterior cingulate cortex, posterior cingulate cortex, precuneus, middle frontal gyrus, middle temporal gyrus, superior parietal lobule, and left angular gyrus. These similar observations were not identified in other three parent-offspring dyads.

CONCLUSIONS

The current study provides biological evidence for greater vulnerability of daughters, but not sons, in developing depression whose mothers have a history of depression. Our findings extend our knowledge on the pathophysiology of major psychiatric conditions that show sex biases and may contribute to the development of novel interventions targeting high-risk individuals.

Individual functional parcellation revealed compensation of dynamic limbic network organization in healthy ageing

Using group-level functional parcellations and constant-length sliding window analysis, dynamic functional connectivity studies have revealed network-specific impairment and compensation in healthy ageing. However, functional parcellation and dynamic time windows vary across individuals; individual-level ageing-related brain dynamics are uncertain. Here, we performed individual parcellation and individual-length sliding window clustering to characterize ageing-related dynamic network changes. Healthy participants (n = 637, 18-88 years) from the Cambridge Centre for Ageing and Neuroscience dataset were included. An individual seven-network parcellation, varied from group-level parcellation, was mapped for each participant. For each network, strong and weak cognitive brain states were revealed by individual-length sliding window clustering and canonical correlation analysis. The results showed negative linear correlations between age and change ratios of sizes in the default mode, frontoparietal, and salience networks and a positive linear correlation between age and change ratios of size in the limbic network (LN). With increasing age, the occurrence and dwell time of strong states showed inverted U-shaped patterns or a linear decreasing pattern in most networks but showed a linear increasing pattern in the LN. Overall, this study reveals a compensative increase in emotional networks (i.e., the LN) and a decline in cognitive and primary sensory networks in healthy ageing. These findings may provide insights into network-specific and individual-level targeting during neuromodulation in ageing and ageing-related diseases.

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.

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.

The Effect of Aging on Brain Glucose Metabolic Connectivity Revealed by [18F]FDG PET-MR and Individual Brain Networks

Contrary to group-based brain connectivity analyses, the aim of this study was to construct individual brain metabolic networks to determine age-related effects on brain metabolic connectivity. Static 40–60 min [¹⁸F]FDG positron emission tomography (PET) images of 67 healthy subjects between 20 and 82 years were acquired with an integrated PET-MR system. Network nodes were defined by brain parcellation using the Schaefer atlas, while connectivity strength between two nodes was determined by comparing the distribution of PET uptake values within each node using a Kullback–Leibler divergence similarity estimation (KLSE). After constructing individual brain networks, a linear and quadratic regression analysis of metabolic connectivity strengths within- and between-networks was performed to model age-dependency. In addition, the age dependency of metrics for network integration (characteristic path length), segregation (clustering coefficient and local efficiency), and centrality (number of hubs) was assessed within the whole brain and within predefined functional subnetworks. Overall, a decrease of metabolic connectivity strength with healthy aging was found within the whole-brain network and several subnetworks except within the somatomotor, limbic, and visual network. The same decrease of metabolic connectivity was found between several networks across the whole-brain network and the functional subnetworks. In terms of network topology, a less integrated and less segregated network was observed with aging, while the distribution and the number of hubs did not change with aging, suggesting that brain metabolic networks are not reorganized during the adult lifespan. In conclusion, using an individual brain metabolic network approach, a decrease in metabolic connectivity strength was observed with healthy aging, both within the whole brain and within several predefined networks. These findings can be used in a diagnostic setting to differentiate between age-related changes in brain metabolic connectivity strength and changes caused by early development of neurodegeneration.

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.

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.

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.

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.

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.

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Multiple large-scale cortical networks converged in the thalamus

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Neurocognitive associated hub existed in the anterior and medial region

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Control-processing hub localized in the intermediate thalamus

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Sensorimotor network was located around the lateral pulvinar nucleus

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.

Individual voxel-based morphometry adjusting covariates in multiple system atrophy

INTRODUCTION

This study aimed to evaluate whether novel individual voxel-based morphometry adjusting covariates (iVAC), such as age, sex, and total intracranial volume, could increase the accuracy of a diagnosis of multiple system atrophy (MSA) and enable the differentiation of MSA from Parkinson's disease (PD).

METHODS

We included 53 MSA patients (MSA-C: 33, MSA-P: 20), 53 PD patients, and 189 healthy controls in this study. All participants underwent high-resolution T1-weighted imaging (WI) and T2-WI with a 3.0-T MRI scanner. We evaluated the occurrence of significant atrophic findings in the pons/middle cerebellar peduncle (MCP) and putamen on iVAC and compared these findings with characteristic changes on T2-WI.

RESULTS

On iVAC, abnormal findings were observed in the pons/MCP of 96.2% of MSA patients and in the putamen of 80% of MSA patients; however, on T2-WI, they were both observed at a frequency of 60.4% in MSA patients. On iVAC, all but one MSA-P patient (98.1%) showed significant atrophic changes in the pons/MCP or putamen. By contrast, 69.8% of patients with MSA showed abnormal signal changes in the pons/MCP or putamen on T2-WI. iVAC yielded 95.0% sensitivity and 96.2% specificity for differentiating MSA-P from PD.

CONCLUSION

iVAC enabled us to recognize the morphological characteristics of MSA visually and with high accuracy compared to T2-WI, indicating that iVAC is a potential diagnostic screening tool for MSA.

Regional glucose metabolic decreases with ageing are associated with microstructural white matter changes: a simultaneous PET/MR study

PURPOSE

Human ageing is associated with a regional reduction in cerebral neuronal activity as assessed by numerous studies on brain glucose metabolism and perfusion, grey matter (GM) density and white matter (WM) integrity. As glucose metabolism may impact energetics to maintain myelin integrity, but changes in functional connectivity may also alter regional metabolism, we conducted a cross-sectional simultaneous FDG PET/MR study in a large cohort of healthy volunteers with a wide age range, to directly assess the underlying associations between reduced glucose metabolism, GM atrophy and decreased WM integrity in a single ageing cohort.

METHODS

In 94 healthy subjects between 19.9 and 82.5 years (mean 50.1 ± 17.1; 47 M/47F, MMSE ≥ 28), simultaneous FDG-PET, structural MR and diffusion tensor imaging (DTI) were performed. Voxel-wise associations between age and grey matter (GM) density, RBV partial-volume corrected (PVC) glucose metabolism, white matter (WM) fractional anisotropy (FA) and mean diffusivity (MD), and age were assessed. Clusters representing changes in glucose metabolism correlating significantly with ageing were used as seed regions for tractography. Both linear and quadratic ageing models were investigated.

RESULTS

An expected age-related reduction in GM density was observed bilaterally in the frontal, lateral and medial temporal cortex, striatum and cerebellum. After PVC, relative FDG uptake was negatively correlated with age in the inferior and midfrontal, cingulate and parietal cortex and subcortical regions, bilaterally. FA decreased with age throughout the entire brain WM. Four white matter tracts were identified connecting brain regions with declining glucose metabolism with age. Within these, relative FDG uptake in both origin and target clusters correlated positively with FA (0.32 ≤ r ≤ 0.71) and negatively with MD (- 0.75 ≤ r ≤  - 0.41).

CONCLUSION

After appropriate PVC, we demonstrated that regional cerebral glucose metabolic declines with age and that these changes are related to microstructural changes in the interconnecting WM tracts. The temporal course and potential causality between ageing effects on glucose metabolism and WM integrity should be further investigated in longitudinal cohort PET/MR studies.

A robust brain signature region approach for episodic memory performance in older adults

This scientific commentary refers to ‘A robust brain signature region approach for episodic memory performance in older adults’ by Fletcher et al. (doi:10.1093/brain/awab007).

Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter?

Brain-predicted age difference (brainPAD) has been used in schizophrenia to assess individual-level deviation in the biological age of the patients' brain (i.e., brain-age) from normative reference brain structural datasets. There is marked inter-study variation in brainPAD in schizophrenia which is commonly attributed to sample heterogeneity. However, the potential contribution of the different machine learning algorithms used for brain-age estimation has not been systematically evaluated. Here, we aimed to assess variation in brain-age estimated by six commonly used algorithms [ordinary least squares regression, ridge regression, least absolute shrinkage and selection operator regression, elastic-net regression, linear support vector regression, and relevance vector regression] when applied to the same brain structural features from the same sample. To assess reproducibility we used data from two publically available samples of healthy individuals (n = 1092 and n = 492) and two further samples, from the Icahn School of Medicine at Mount Sinai (ISMMS) and the Center of Biomedical Research Excellence (COBRE), comprising both patients with schizophrenia (n = 90 and n = 76) and healthy individuals (n = 200 and n = 87). Performance similarity across algorithms was compared within each sample using correlation analyses and hierarchical clustering. Across all samples ordinary least squares regression, the only algorithm without a penalty term, performed markedly worse. All other algorithms showed comparable performance but they still yielded variable brain-age estimates despite being applied to the same data. Although brainPAD was consistently higher in patients with schizophrenia, it varied by algorithm from 3.8 to 5.2 years in the ISMMS sample and from to 4.5 to 11.7 years in the COBRE sample. Algorithm choice introduces variations in brain-age and may confound inter-study comparisons when assessing brainPAD in schizophrenia.

Single-Pulse Transcranial Magnetic Stimulation-Evoked Potential Amplitudes and Latencies in the Motor and Dorsolateral Prefrontal Cortex among Young, Older Healthy Participants, and Schizophrenia Patients

Background: The combination of transcranial magnetic stimulation (TMS) with electroencephalography (EEG) allows for non-invasive investigation of cortical response and connectivity in human cortex. This study aimed to examine the amplitudes and latencies of each TMS-evoked potential (TEP) component induced by single-pulse TMS (spTMS) to the left motor (M1) and dorsolateral prefrontal cortex (DLPFC) among healthy young participants (YNG), older participants (OLD), and patients with schizophrenia (SCZ). Methods: We compared the spatiotemporal characteristics of TEPs induced by spTMS among the groups. Results: Compared to YNG, M1-spTMS induced lower amplitudes of N45 and P180 in OLD and a lower amplitude of P180 in SCZ, whereas the DLPFC-spTMS induced a lower N45 in OLD. Further, OLD demonstrated latency delays in P60 after M1-spTMS and in N45-P60 over the right central region after left DLPFC-spTMS, whereas SCZ demonstrated latency delays in N45-P60 over the midline and right central regions after DLPFC-spTMS. Conclusions: These findings suggest that inhibitory and excitatory mechanisms mediating TEPs may be altered in OLD and SCZ. The amplitude and latency changes of TEPs with spTMS may reflect underlying neurophysiological changes in OLD and SCZ, respectively. The spTMS administered to M1 and the DLPFC can probe cortical functions by examining TEPs. Thus, TMS-EEG can be used to study changes in cortical connectivity and signal propagation from healthy to pathological brains.

Pattern of cortical thinning in logopenic progressive aphasia patients in Thailand

Background

Logopenic progressive aphasia (LPA) is an uncommon neurodegenerative disorder primarily characterized by word-finding difficulties and sentence repetition impairment. Prominent cortical atrophy around left temporo-parietal junction (TPJ) is a classical imaging feature of LPA. This study investigated cortical thinning pattern in clinically diagnosed LPA patients using non-demented subjects as a control group. We also aimed to explore whether there was prominent thinning of other cortical area additional to the well-recognized left TPJ.

Methods

Thicknesses of all cortical regions were measured from brain magnetic resonance images using an automated command on Freesurfer software. Cortical thickness of the LPA and control groups were compared by two methods: 1) using a general linear model (GLM) in SPSS software; and 2) using a vertex-by-vertex GLM, performed with Freesurfer’s QDEC interface.

Results

Besides the well-recognized left TPJ, cortical regions that were significantly thinner in the LPA group by both comparison methods included left caudal middle frontal gyrus (CMFG) (p = 0.006 by SPSS, p = 0.0003 by QDEC), left rostral middle frontal gyrus (p = 0.001 by SPSS, p = 0.0001 by QDEC), left parahippocampal gyrus (p = 0.008 by SPSS, p = 0.005 by QDEC) and right CMFG (p = 0.005 by SPSS, p = 0.0001 by QDEC).

Conclusions

Our results demonstrated that thinning of middle frontal gyri may be an additional feature in clinically diagnosed LPA patients. Involvement of left parahippocampal gyrus may reflect the underlying neuropathology of Alzheimer’s disease in majority of the LPA patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-020-02039-x.

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.

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.

Affect in the Aging Brain: A Neuroimaging Meta-Analysis of Older Vs. Younger Adult Affective Experience and Perception

We report the first functional neuroimaging meta-analysis on age-related differences in adult neural activity during affect. We identified and coded experimental contrasts from 27 studies (published 1997-2018) with 490 older adults (55-87 years, M age  = 69 years) and 470 younger adults (18-39 years, M age  = 24 years). Using multilevel kernel density analysis, we assessed functional brain activation contrasts for older vs. younger adult affect across in-scanner tasks (i.e., affect induction and perception). Relative to older adults, younger adults showed more reliable activation in subcortical structures (e.g., amygdala, thalamus, caudate) and in relatively more posterior aspects of specific brain structures (e.g., posterior insula, mid- and posterior cingulate). In contrast, older adults exhibited more reliable activation in the prefrontal cortex and more anterior aspects of specific brain structures (e.g., anterior insula, anterior cingulate). Meta-analytic coactivation network analyses further revealed that in younger adults, the amygdala and mid-cingulate were more central, locally efficient network nodes, whereas in older adults, regions in the superior and medial prefrontal cortex were more central, locally efficient network nodes. Collectively, these findings help characterize age differences in the brain basis of affect and provide insights for future investigations into the neural mechanisms underlying affective aging.

Individual changes in visual performance in non-demented Parkinson's disease patients: a 1-year follow-up study

Cognitive deficits in Parkinson's disease (PD) are heterogeneous entities, and the cognitive status fluctuates over time. However, individual changes in longitudinal cognitive performance in PD are not fully understood. We evaluated three visual indices (visuoperception, visuoconstruction, and visuospatial ability) and four cognitive domains (attention/working memory, executive function, memory, and language) at baseline (Time1) and at 1-year follow-up (Time2) in 36 patients with PD and 32 healthy controls (HCs). To explore the magnitude and frequency of cognitive changes, we analyzed data using the simple difference method and the standardized regression-based method. We also explored the correlations between changes in test scores and several clinical predictors, using logistic regression analysis. At 1 year, patients with PD showed higher rates of change in scores on several cognitive tests, especially the Incomplete Letters test of visuoperception, compared to HCs. After adjusting for demographic variables, the visuoperceptual change was 61.1% overall, with the largest effect size. The changes in scores of visuoperception correlated with those of memory (r = 0.672, p < 0.001), language (r = 0.389, p < 0.05), and visuospatial ability (r = 0.379, p < 0.05). The severity of olfactory disturbance, the MDS-UPDRS Part I score, and younger PD onset predicted the significant changes observed in the Incomplete Letters test scores. Visuoperception changed more in non-demented PD patients than in HCs at 1-year follow-up. The changes in visuoperception could relate to involvement of the ventral occipitotemporal pathway, the more widespread temporal lobe, and brain reserve in PD.

Brain structure changes over time in normal and mildly impaired aged persons

Structural brain changes in aging are known to occur even in the absence of dementia, but the magnitudes and regions involved vary between studies. To further characterize these changes, we analyzed paired MRI images acquired with identical protocols and scanner over a median 5.8-year interval. The normal study group comprised 78 elders (25M 53F, baseline age range 70–78 years) who underwent an annual standardized expert assessment of cognition and health and who maintained normal cognition for the duration of the study. We found a longitudinal grey matter (GM) loss rate of 2.56 ± 0.07 ml/year (0.20 ± 0.04%/year) and a cerebrospinal fluid (CSF) expansion rate of 2.97 ± 0.07 ml/year (0.22 ± 0.04%/year). Hippocampal volume loss rate was higher than the GM and CSF global rates, 0.0114 ± 0.0004 ml/year (0.49 ± 0.04%/year). Regions of greatest GM loss were posterior inferior frontal lobe, medial parietal lobe and dorsal cerebellum. Rates of GM loss and CSF expansion were on the low end of the range of other published values, perhaps due to the relatively good health of the elder volunteers in this study. An additional smaller group of 6 subjects diagnosed with MCI at baseline were followed as well, and comparisons were made with the normal group in terms of both global and regional GM loss and CSF expansion rates. An increased rate of GM loss was found in the hippocampus bilaterally for the MCI group.

Age-related structural and functional variations in 5,967 individuals across the adult lifespan

Exploring brain changes across the human lifespan is becoming an important topic in neuroscience. Though there are multiple studies which investigated the relationship between age and brain imaging, the results are heterogeneous due to small sample sizes and relatively narrow age ranges. Here, based on year-wise estimation of 5,967 subjects from 13 to 72 years old, we aimed to provide a more precise description of adult lifespan variation trajectories of gray matter volume (GMV), structural network correlation (SNC), and functional network connectivity (FNC) using independent component analysis and multivariate linear regression model. Our results revealed the following relationships: (a) GMV linearly declined with age in most regions, while parahippocampus showed an inverted U-shape quadratic relationship with age; SNC presented a U-shape quadratic relationship with age within cerebellum, and inverted U-shape relationship primarily in the default mode network (DMN) and frontoparietal (FP) related correlation. (b) FNC tended to linearly decrease within resting-state networks (RSNs), especially in the visual network and DMN. Early increase was revealed between RSNs, primarily in FP and DMN, which experienced a decrease at older ages. U-shape relationship was also revealed to compensate for the cognition deficit in attention and subcortical related connectivity at late years. (c) The link between middle occipital gyrus and insula, as well as precuneus and cerebellum, exhibited similar changing trends between SNC and FNC across the adult lifespan. Collectively, these results highlight the benefit of lifespan study and provide a precise description of age-related regional variation and SNC/FNC changes based on a large dataset.

Changes in white matter fiber density and morphology across the adult lifespan: A cross-sectional fixel-based analysis

White matter (WM) fiber bundles change dynamically with age. These changes could be driven by alterations in axonal diameter, axonal density, and myelin content. In this study, we applied a novel fixel‐based analysis (FBA) framework to examine these changes throughout the adult lifespan. Using diffusion‐weighted images from a cohort of 293 healthy volunteers (89 males/204 females) from ages 21 to 86 years old, we performed FBA to analyze age‐related changes in microscopic fiber density (FD) and macroscopic fiber morphology (fiber cross section [FC]). Our results showed significant and widespread age‐related alterations in FD and FC across the whole brain. Interestingly, some fiber bundles such as the anterior thalamic radiation, corpus callosum, and superior longitudinal fasciculus only showed significant negative relationship with age in FD values, but not in FC. On the other hand, some segments of the cerebello‐thalamo‐cortical pathway only showed significant negative relationship with age in FC, but not in FD. Analysis at the tract‐level also showed that major fiber tract groups predominantly distributed in the frontal lobe (cingulum, forceps minor) exhibited greater vulnerability to the aging process than the others. Differences in FC and the combined measure of FD and cross section values observed between sexes were mostly driven by differences in brain sizes although male participants tended to exhibit steeper negative linear relationship with age in FD as compared to female participants. Overall, these findings provide further insights into the structural changes the brain's WM undergoes due to the aging process.

Alterations in Cognition-Related Cerebello-Cerebral Networks in Multiple System Atrophy

We aimed to elucidate the effect of cerebellar degeneration in relation to cognition in multiple system atrophy (MSA). Thirty-two patients diagnosed with probable MSA and 32 age- and gender-matched healthy controls (HCs) were enrolled. We conducted voxel-based morphometry (VBM) for anatomical images and independent component analysis (ICA), dual-regression analysis, and seed-based analysis for functional images with voxel-wise gray matter correction. In the MSA group, a widespread cerebellar volume loss was observed. ICA and dual-regression analysis showed lower functional connectivity (FC) in the left executive control and salience networks in regions located in the cerebellum. Seed-based analysis using the identified cerebellar regions as seeds showed extensive disruptions in cerebello-cerebral networks. Global cognitive scores correlated with the FC values between the right lobules VI/crus I and the medial prefrontal/anterior cingulate cortices and between the same region and the amygdala/parahippocampal gyrus. Our study indicates that cerebellar degeneration in MSA causes segregation of cerebellar-cerebral networks. Furthermore, the cognitive deficits in MSA may be driven by decreased cerebello-prefrontal and cerebello-amygdaloid functional connections.

Predicting Brain Age of Healthy Adults Based on Structural MRI Parcellation Using Convolutional Neural Networks

Structural magnetic resonance imaging (MRI) studies have demonstrated that the brain undergoes age-related neuroanatomical changes not only regionally but also on the network level during the normal development and aging process. In recent years, many studies have focused on estimating age using structural MRI measurements. However, the age prediction effects on different structural networks remain unclear. In this study, we established age prediction models based on common structural networks using convolutional neural networks (CNN) with data from 1,454 healthy subjects aged 18–90 years. First, based on the reference map of CorticalParcellation_Yeo2011, we obtained structural network images for each subject, including images of the following: the frontoparietal network (FPN), the dorsal attention network (DAN), the default mode network (DMN), the somatomotor network (SMN), the ventral attention network (VAN), the visual network (VN), and the limbic network (LN). Then, we built a 3D CNN model for each structural network using a large training dataset (n = 1,303) and the predicted ages of the subjects in the test dataset (n = 151). Finally, we estimated the age prediction performance of CNN compared with Gaussian process regression (GPR) and relevance vector regression (RVR). The results of CNN showed that the FPN, DAN, and DMN exhibited the optimal age prediction accuracies with mean absolute errors (MAEs) of 5.55 years, 5.77 years, and 6.07 years, respectively, and the other four networks, i.e., the SMN, VAN, VN, and LN, tended to have larger MAEs of more than 8 years. With respect to GPR and RVR, the top three prediction accuracies were still from the FPN, DAN, and DMN; moreover, CNN made more precise predictions than GPR and RVR for these three networks. Our findings suggested that CNN has the optimal age prediction performance, and our age prediction model can be potentially used for brain disorder diagnosis according to age prediction differences.

Semantic deficits in ALS related to right lingual/fusiform gyrus network involvement

BACKGROUND

The clinicopathological continuity between amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) is well known. Although ALS demonstrates language symptoms similar to FTLD, including semantic dementia, word reading impairments in ALS have not been well studied. "Jukujikun" are Kanji-written words with irregular pronunciation comparable to "exception words" and useful for detecting semantic deficits in Japan. We conducted a cross-sectional study to investigate Jukujikun reading impairments and related network changes in ALS.

METHODS

We enrolled 71 ALS patients and 69 healthy controls (HCs). Age-, sex-, and education matched HCs were recruited from another cohort study concurrently with patient registration. We examined neuropsychological factors including low frequency Jukujikun reading. We performed resting-state functional magnetic resonance imaging with voxel-based graph analysis on a subset of participants who agreed.

FINDINGS

Low frequency Jukujikun score was decreased in ALS (15·0[11·0-19·0](median[25-75 percentile])) compared with HCs (19·0[17·3-20·0]) (p < 0·001, effect size = 0·43). Fifty-two percent of ALS (N = 37) with low frequency Jukujikun score ≤ 5th percentile of HCs was classified as ALS with positive Jukujikun deficit (ALS-JD+). Compared with HCs, ALS-JD+ showed decreased degree centrality in the right lingual/fusiform gyrus, where connectivities with regions associated with word perception, semantic processing, or speech production were decreased. They also showed increased degree centrality in the left inferior/middle temporal gyrus, associated with increased connectivities involving semantic processing.

INTERPRETATION

Dysfunction of the "hub" in the right lingual/fusiform gyrus can affect semantic deficit in ALS. Considering neuropsychological symptoms as network impairments is vital for understanding various diseases. FUND: MHLW and MEXT, Japan.

Reorganization of brain networks and its association with general cognitive performance over the adult lifespan

Healthy aging is associated with structural and functional changes in the brain even in individuals who are free of neurodegenerative diseases. Using resting state functional magnetic resonance imaging data from a carefully selected cohort of participants, we examined cross sectional changes in the functional organization of several large-scale brain networks over the adult lifespan and its potential association with general cognitive performance. Converging results from multiple analyses at the voxel, node, and network levels showed widespread reorganization of functional brain networks with increasing age. Specifically, the primary processing (visual and sensorimotor) and visuospatial (dorsal attention) networks showed diminished network integrity, while the so-called core neurocognitive (executive control, salience, and default mode) and basal ganglia networks exhibited relatively preserved between-network connections. The visuospatial and precuneus networks also showed significantly more widespread increased connectivity with other networks. Graph analysis suggested that this reorganization progressed towards a more integrated network topology. General cognitive performance, assessed by Addenbrooke's Cognitive Examination-Revised total score, was positively correlated with between-network connectivity among the core neurocognitive and basal ganglia networks and the integrity of the primary processing and visuospatial networks. Mediation analyses further indicated that the observed association between aging and relative decline in cognitive performance could be mediated by changes in relevant functional connectivity measures. Overall, these findings provided further evidence supporting widespread age-related brain network reorganization and its potential association with general cognitive performance during healthy aging.