Advanced brain aging in Parkinson's disease with cognitive impairment.

Patients with Parkinson's disease and cognitive impairment (PD-CI) deteriorate faster than those without cognitive impairment (PD-NCI), suggesting an underlying difference in the neurodegeneration process. We aimed to verify brain age differences in PD-CI and PD-NCI and their clinical significance. A total of 94 participants (PD-CI, n = 27; PD-NCI, n = 34; controls, n = 33) were recruited. Predicted age difference (PAD) based on gray matter (GM) and white matter (WM) features were estimated to represent the degree of brain aging. Patients with PD-CI showed greater GM-PAD (7.08 ± 6.64 years) and WM-PAD (8.82 ± 7.69 years) than those with PD-NCI (GM: 1.97 ± 7.13, Padjusted = 0.011; WM: 4.87 ± 7.88, Padjusted = 0.049) and controls (GM: -0.58 ± 7.04, Padjusted = 0.004; WM: 0.88 ± 7.45, Padjusted = 0.002) after adjusting demographic factors. In patients with PD, GM-PAD was negatively correlated with MMSE (Padjusted = 0.011) and MoCA (Padjusted = 0.013) and positively correlated with UPDRS Part II (Padjusted = 0.036). WM-PAD was negatively correlated with logical memory of immediate and delayed recalls (Padjusted = 0.003 and Padjusted < 0.001). Also, altered brain regions in PD-CI were identified and significantly correlated with brain age measures, implicating the neuroanatomical underpinning of neurodegeneration in PD-CI. Moreover, the brain age metrics can improve the classification between PD-CI and PD-NCI. The findings suggest that patients with PD-CI had advanced brain aging that was associated with poor cognitive functions. The identified neuroimaging features and brain age measures can serve as potential biomarkers of PD-CI.

Brain Age Is Associated with Cognitive Outcomes of Cholinesterase Inhibitor Treatment in Patients with Mild Cognitive Impairment.

Background: The effect of cholinesterase inhibitor (ChEI) on mild cognitive impairment (MCI) is controversial. Brain age has been shown to predict Alzheimer's disease conversion from MCI. Objective: The study aimed to show that brain age is related to cognitive outcomes of ChEI treatment in MCI. Methods: Brain MRI, the Clinical Dementia Rating (CDR) and Mini-Mental State Exam (MMSE) scores were retrospectively retrieved from a ChEI treatment database. Patients who presented baseline CDR of 0.5 and received ChEI treatment for at least 2 years were selected. Patients with stationary or improved cognition as verified by the CDR and MMSE were categorized to the ChEI-responsive group, and those with worsened cognition were assigned to the ChEI-unresponsive group. A gray matter brain age model was built with a machine learning algorithm by training T1-weighted MRI data of 362 healthy participants. The model was applied to each patient to compute predicted age difference (PAD), i.e. the difference between brain age and chronological age. The PADs were compared between the two groups. Results: 58 patients were found to fit the ChEI-responsive criteria in the patient data, and 58 matched patients that fit the ChEI-unresponsive criteria were compared. ChEI-unresponsive patients showed significantly larger PAD than ChEI-responsive patients (8.44±8.78 years versus 3.87±9.02 years, p = 0.0067). Conclusions: Gray matter brain age is associated with cognitive outcomes after 2 years of ChEI treatment in patients with the CDR of 0.5. It might facilitate the clinical trials of novel therapeutics for MCI.

Enhanced white matter fiber tract of the cortical visual system in visual artists: implications for creativity.

Introduction: This study aimed to examine the white matter characteristics of visual artists (VAs) in terms of visual creativity and the structural connectivity within the cortical visual system. Methods: Diffusion spectrum imaging was utilized to examine the changes in white matter within the cortical visual system of a group of VAs (n = 25) in comparison to a group of healthy controls matched for age and education (n = 24). To assess the integrity of white matter and its relationship with visual creativity, we conducted a comprehensive analysis using region-based and track-specific tractographic examinations. Results: Our study uncovered that VAs demonstrated increased normalized quantitative anisotropy in specific brain regions, including the right inferior temporal gyrus and right lateral occipital gyrus, along with the corresponding white matter fiber tracts connecting these regions. These enhancements within the cortical visual system were also found to be correlated with measures of visual creativity obtained through psychological assessments. Discussion: The noted enhancement in the white matter within the cortical visual system of VAs, along with its association with visual creativity, is consistent with earlier research demonstrating heightened functional connectivity in the same system among VAs. Our study's findings suggest a link between the visual creativity of VAs and structural alterations within the brain's visual system.

The microstructural change of the brain and its clinical severity association in pediatric Tourette syndrome patients.

BACKGROUND: Gilles de la Tourette syndrome (GTS) is a prevalent pediatric neurological disorder. Most studies point to abnormalities in the cortico-striato-thalamocortical (CSTC) circuits. Neuroimaging studies have shown GTS's extensive impact on the entire brain. However, due to participant variability and potential drug and comorbidity impact, the results are inconsistent. To mitigate the potential impact of participant heterogeneity, we excluded individuals with comorbidities or those currently undergoing medication treatments. Based on the hypothesis of abnormality within the CSTC circuit, we investigated microstructural changes in white matter using diffusion spectrum imaging (DSI). This study offers the first examination of microstructural changes in treatment-naïve pediatric patients with pure GTS using diffusion spectrum imaging. METHODS: This single-center prospective study involved 30 patients and 30 age- and gender-matched healthy volunteers who underwent sagittal T1-weighted MRI and DSI. We analyzed generalized fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. RESULTS: No significant differences were observed in mean diffusivity and axial diffusivity values between the two groups. However, the patient group exhibited significantly higher generalized fractional anisotropy values in the right frontostriatal tract of the dorsolateral prefrontal cortex, the right frontostriatal tract of the precentral gyrus, and bilateral thalamic radiation of the dorsolateral prefrontal cortex. Additionally, the generalized fractional anisotropy value of the right frontostriatal tract of the precentral gyrus is inversely correlated with the total tic severity scores at the most severe condition. CONCLUSION: Treatment-naïve pediatric GTS patients demonstrated increased connectivity within the CSTC circuit as per diffusion spectrum imaging, indicating possible CSTC circuit dysregulation. This finding could also suggest a compensatory change. It thus underscores the necessity of further investigation into the fundamental pathological changes in GTS. Nevertheless, the observed altered connectivity in GTS patients might serve as a potential target for therapeutic intervention.

Differences in white matter segments in autistic males, non-autistic siblings, and non-autistic participants: An intermediate phenotype approach.

LAY ABSTRACT: White matter is the neural pathway that connects neurons in different brain regions. Although research has shown white matter differences between autistic and non-autistic people, little is known about the properties of white matter in non-autistic siblings. In addition, past studies often focused on the whole neural tracts; it is unclear where differences exist in specific segments of the tracts. This study identified neural segments that differed between autistic people, their non-autistic siblings, and the age- and non-autistic people. We found altered segments within the tracts connected to anterior brain regions corresponding to several higher cognitive functions (e.g. executive functions) in autistic people and non-autistic siblings. Segments connecting to regions for social cognition and Theory of Mind were altered only in autistic people, explaining a large portion of autistic traits and may serve as neuroimaging markers. Segments within the tracts associated with fewer autistic traits or connecting brain regions for diverse highly integrated functions showed compensatory increases in the microstructural properties in non-autistic siblings. Our findings suggest that differential white matter segments that are shared between autistic people and non-autistic siblings may serve as potential "intermediate phenotypes"-biological or neuropsychological characteristics in the causal link between genetics and symptoms-of autism. These findings shed light on a promising neuroimaging model to refine the intermediate phenotype of autism which may facilitate further identification of the genetic and biological bases of autism. Future research exploring links between compensatory segments and neurocognitive strengths in non-autistic siblings may help understand brain adaptation to autism.

Atypical development in white matter microstructures in ADHD: A longitudinal diffusion imaging study.

BACKGROUND: In cross-sectional studies, alterations in white matter microstructure are evident in children with attention-deficit/hyperactivity disorder (ADHD) but not so prominent in adults with ADHD compared to typically-developing controls (TDC). Moreover, the developmental trajectories of white matter microstructures in ADHD are unclear, given the limited longitudinal imaging studies that characterize developmental changes in ADHD vs. TDC. METHODS: This longitudinal study acquired diffusion spectrum imaging (DSI) at two time points. The sample included 55 participants with ADHD and 61 TDC. The enrollment/first DSI age ranged from 7 to 18 years, with a five-year mean follow-up time. We examined time-by-diagnosis interaction on the generalized fractional anisotropy (GFA) of 45 white matter tracts, adjusting for confounding factors and correcting for multiple comparisons. We also tested whether the longitudinal changes of microstructures were associated with ADHD symptoms and attention performance in a computerized continuous performance test. RESULTS: Participants with ADHD showed more rapid development of GFA in the arcuate fasciculus, superior longitudinal fasciculus, frontal aslant tract, cingulum, inferior fronto-occipital fasciculus (IFOF), frontostriatal tract connecting the prefrontal cortex (FS-PFC), thalamic radiation, corticospinal tract, and corpus callosum. Within participants with ADHD, more rapid GFA increases in cingulum and FS-PFC were associated with slower decreases in inattention symptoms. In addition, in all participants, more rapid GFA increases in cingulum and IFOF were associated with greater improvement in attention performance. CONCLUSION: Our findings suggest atypical developmental trajectories of white matter tracts in ADHD, characterized by normalization and possible compensatory neuroplastic processes with age from childhood to early adulthood.

Long-term musical training induces white matter plasticity in emotion and language networks.

Numerous studies have reported that long-term musical training can affect brain functionality and induce structural alterations in the brain. Singing is a form of vocal musical expression with an unparalleled capacity for communicating emotion; however, there has been relatively little research on neuroplasticity at the network level in vocalists (i.e., noninstrumental musicians). Our objective in this study was to elucidate changes in the neural network architecture following long-term training in the musical arts. We employed a framework based on graph theory to depict the connectivity and efficiency of structural networks in the brain, based on diffusion-weighted images obtained from 35 vocalists, 27 pianists, and 33 nonmusicians. Our results revealed that musical training (both voice and piano) could enhance connectivity among emotion-related regions of the brain, such as the amygdala. We also discovered that voice training reshaped the architecture of experience-dependent networks, such as those involved in vocal motor control, sensory feedback, and language processing. It appears that vocal-related changes in areas such as the insula, paracentral lobule, supramarginal gyrus, and putamen are associated with functional segregation, multisensory integration, and enhanced network interconnectivity. These results suggest that long-term musical training can strengthen or prune white matter connectivity networks in an experience-dependent manner.

DACO: Distortion/artefact correction for diffusion MRI data.

In this paper, we propose a registration-based algorithm to correct various distortions or artefacts (DACO) commonly observed in diffusion-weighted (DW) magnetic resonance images (MRI). The registration in DACO is accomplished by means of a pseudo b0 image, which is synthesized from the anatomical images such as T1-weighted image or T2-weighted image, and a pseudo diffusion MRI (dMRI) data, which is derived from the Gaussian model of diffusion tensor imaging (DTI) or the Hermite model of mean apparent propagator (MAP)-MRI. DACO corrects (1) the susceptibility-induced distortions and (2) the misalignment between the dMRI data and anatomical images by registering the real b0 image to the pseudo b0 image, and corrects (3) the eddy current-induced distortions and (4) the head motions by registering each image in the real dMRI data to the corresponding image in the pseudo dMRI data. DACO estimates the models of artefacts simultaneously in an iterative and interleaved manner. The mathematical formulation of the models and the estimation procedures are detailed in this paper. Using the human connectome project (HCP) data the evaluation shows that DACO could estimate the model parameters accurately. Furthermore, the evaluation conducted on the real human data acquired from clinical MRI scanners reveals that the method could reduce the artefacts effectively. The DACO method leverages the anatomical image, which is routinely acquired in clinical practice, to correct the artefacts, omitting the additional acquisitions needed to conduct the algorithm. Therefore, our method should be beneficial to most dMRI data, particularly to those acquired without field maps or reverse phase-encoding images.

Neurodevelopmental model of schizophrenia revisited: similarity in individual deviation and idiosyncrasy from the normative model of whole-brain white matter tracts and shared brain-cognition covariation with ADHD and ASD.

The neurodevelopmental model of schizophrenia is supported by multi-level impairments shared among schizophrenia and neurodevelopmental disorders. Despite schizophrenia and typical neurodevelopmental disorders, i.e., autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), as disorders of brain dysconnectivity, no study has ever elucidated whether whole-brain white matter (WM) tracts integrity alterations overlap or diverge between these three disorders. Moreover, whether the linked dimensions of cognition and brain metrics per the Research Domain Criteria framework cut across diagnostic boundaries remains unknown. We aimed to map deviations from normative ranges of whole-brain major WM tracts for individual patients to investigate the similarity and differences among schizophrenia (281 patients subgrouped into the first-episode, subchronic and chronic phases), ASD (175 patients), and ADHD (279 patients). Sex-specific WM tract normative development was modeled from diffusion spectrum imaging of 626 typically developing controls (5-40 years). There were three significant findings. First, the patterns of deviation and idiosyncrasy of WM tracts were similar between schizophrenia and ADHD alongside ASD, particularly at the earlier stages of schizophrenia relative to chronic stages. Second, using the WM deviation patterns as features, schizophrenia cannot be separated from neurodevelopmental disorders in the unsupervised machine learning algorithm. Lastly, the canonical correlation analysis showed schizophrenia, ADHD, and ASD shared linked cognitive dimensions driven by WM deviations. Together, our results provide new insights into the neurodevelopmental facet of schizophrenia and its brain basis. Individual's WM deviations may contribute to diverse arrays of cognitive function along a continuum with phenotypic expressions from typical neurodevelopmental disorders to schizophrenia.

Brain white matter correlates of learning ankle tracking using a wearable device: importance of the superior longitudinal fasciculus II.

BACKGROUND: Wearable devices have been found effective in training ankle control in patients with neurological diseases. However, the neural mechanisms associated with using wearable devices for ankle training remain largely unexplored. This study aimed to investigate the ankle tracking performance and brain white matter changes associated with ankle tracking learning using a wearable-device system and the behavior-brain structure relationships in middle-aged and older adults. METHODS: Twenty-six middle-aged and older adults (48-75 years) participated in this study. Participants underwent 5-day ankle tracking learning with their non-dominant foot using a custom-built ankle tracking system equipped with a wearable sensor and a sensor-computer interface for real-time visual feedback and data acquisition. Repeated and random sequences of target tracking trajectories were both used for learning and testing. Ankle tracking performance, calculated as the root-mean-squared-error (RMSE) between the target and actual ankle trajectories, and brain diffusion spectrum MR images were acquired at baseline and retention tests. The general fractional anisotropy (GFA) values of eight brain white matter tracts of interest were calculated to indicate their integrity. Two-way (Sex × Time) mixed repeated measures ANOVA procedures were used to investigate Sex and Time effects on RMSE and GFA. Correlations between changes in RMSE and those in GFA were analyzed, controlling for age and sex. RESULTS: After learning, both male and female participants reduced the RMSE of tracking repeated and random sequences (both p < 0.001). Among the eight fiber tracts, the right superior longitudinal fasciculus II (R SLF II) was the only one which showed both increased GFA (p = 0.039) after learning and predictive power of reductions in RMSE for random sequence tracking with its changes in GFA [ß = 0.514, R2 change = 0.259, p = 0.008]. CONCLUSIONS: Our findings implied that interactive tracking movement learning using wearable sensors may place high demands on the attention, sensory feedback integration, and sensorimotor transformation functions of the brain. Therefore, the SLF II, which is known to perform these brain functions, showed corresponding neural plasticity after such learning, and its plasticity also predicted the behavioral gains. The SLF II appears to be a very important anatomical neural correlate involved in such learning paradigms.

Advanced brain aging in multiple system atrophy compared to Parkinson's disease.

Multiple system atrophy (MSA) and Parkinson's disease (PD) belong to alpha-synucleinopathy, but they have very different clinical courses and prognoses. An imaging biomarker that can differentiate between the two diseases early in the disease course is desirable for appropriate treatment. Neuroimaging-based brain age paradigm provides an individualized marker to differentiate aberrant brain aging patterns in neurodegenerative diseases. In this study, patients with MSA (N = 23), PD (N = 33), and healthy controls (N = 34; HC) were recruited. A deep learning approach was used to estimate brain-predicted age difference (PAD) of gray matter (GM) and white matter (WM) based on image features extracted from T1-weighted and diffusion-weighted magnetic resonance images, respectively. Spatial normative models of image features were utilized to quantify neuroanatomical impairments in patients, which were then used to estimate the contributions of image features to brain age measures. For PAD of GM (GM-PAD), patients with MSA had significantly older brain age (9.33 years) than those with PD (0.75 years; P = 0.002) and HC (-1.47 years; P < 0.001), and no significant difference was found between PD and HC (P = 1.000). For PAD of WM (WM-PAD), it was significantly greater in MSA (9.27 years) than that in PD (1.90 years; P = 0.037) and HC (-0.74 years; P < 0.001); there was no significant difference between PD and HC (P = 0.087). The most salient image features that contributed to PAD in MSA and PD were different. For GM, they were the orbitofrontal regions and the cuneus in MSA and PD, respectively, and for WM, they were the central corpus callosum and the uncinate fasciculus in MSA and PD, respectively. Our results demonstrated that MSA revealed significantly greater PAD than PD, which might be related to markedly different neuroanatomical contributions to brain aging. The image features with distinct contributions to brain aging might be of value in the differential diagnosis of MSA and PD.

Validation of neuroimaging-based brain age gap as a mediator between modifiable risk factors and cognition.

Neuroimaging-based brain age gap (BAG) is presumably a mediator linking modifiable risk factors to cognitive changes, but this has not been verified yet. To address this hypothesis, modality-specific brain age models were constructed and applied to a population-based cohort (N = 326) to estimate their BAG. Structural equation modeling was employed to investigate the mediation effect of BAG between modifiable risk factors (assessed by 2 cardiovascular risk scores) and cognitive functioning (examined by 4 cognitive assessments). The association between higher burden of modifiable risk factors and poorer cognitive functioning can be significantly mediated by a larger BAG (multimodal: p = 0.014, 40.8% mediation proportion; white matter-based: p = 0.023, 15.7% mediation proportion), which indicated an older brain. Subgroup analysis further revealed a steeper slope (p = 0.019) of association between cognitive functioning and multimodal BAG in the group of higher modifiable risks. The results confirm that BAG can serve as a mediating indicator linking risk loadings to cognitive functioning, implicating its potential in the management of cognitive aging and dementia.

Detection of advanced brain aging in schizophrenia and its structural underpinning by using normative brain age metrics.

Conceptualizing mental disorders as deviations from normative functioning provides a statistical perspective for understanding the individual heterogeneity underlying psychiatric disorders. To broaden the understanding of the idiosyncrasy of brain aging in schizophrenia, we introduced an imaging-derived brain age paradigm combined with normative modeling as novel brain age metrics. We constructed brain age models based on GM, WM, and their combination (multimodality) features of 482 normal participants. The normalized predicted age difference (nPAD) was estimated in 147 individuals with schizophrenia and their 130 demographically matched controls through normative models of brain age metrics and compared between the groups. Regression analyses were also performed to investigate the associations of nPAD with illness duration, onset age, symptom severity, and intelligence quotient. Finally, regional contributions to advanced brain aging in schizophrenia were investigated. The results showed that the individuals exhibited significantly higher nPAD (P < 0.001), indicating advanced normative brain age than the normal controls in GM, WM, and multimodality models. The nPAD measure based on WM was positively associated with the negative symptom score (P = 0.009), and negatively associated with the intelligence quotient (P = 0.039) and onset age (P = 0.006). The imaging features that contributed to nPAD mostly involved the prefrontal, temporal, and parietal lobes, especially the precuneus and uncinate fasciculus. This study demonstrates that normative brain age metrics could detect advanced brain aging and associated clinical and neuroanatomical features in schizophrenia. The proposed nPAD measures may be useful to investigate aberrant brain aging in mental disorders and their brain-phenotype relationships.

Altered White-matter Tract Property in Adults with Attention-deficit Hyperactivity Disorder.

Although altered microstructure properties of white-matter tracts have been reported in children with attention-deficit/hyperactivity disorder (ADHD), findings from relatively few adult ADHD studies are inconsistent. This study aims to examine microstructural property over the whole brain in adults with ADHD and explore structural connectivities. Sixty-four medication-naïve adults with ADHD and 81 healthy adults received diffusion spectrum imaging. Generalized fractional anisotropy (GFA), an index indicating microstructural property, was calculated stepwise among 76 white-matter tracts. With the threshold-free clustering weighted method, the segments with the largest group difference were selected, and mean GFA (mGFA) values were calculated. Adults with ADHD had increased mGFA values in the segments located in the left frontal aslant tract, the right inferior longitudinal fasciculus, and the left perpendicular fasciculus, and reduced mGFA values in the segments located in the right superior longitudinal fasciculus (SLF) I, the left SLF II, the right frontostriatal tracts from dorsolateral prefrontal cortex and the ventrolateral prefrontal cortex, the right medial lemniscus, the right inferior thalamic radiation to the auditory cortex, and the callosal fibers. Additionally, the mGFA value of the right SLF I segment was associated with hyperactivity-impulsivity symptoms. Our findings suggest that white-matter tracts with altered microstructure properties are located within the attention networks, fronto-striato-thalamocortical regions, and those associated with attention and visual perception in adults with ADHD.

Brain Age Difference at Baseline Predicts Clinical Dementia Rating Change in Approximately Two Years.

BACKGROUND: The Clinical Dementia Rating (CDR) has been widely used to assess dementia severity, but it is limited in predicting dementia progression, thus unable to advise preventive measures to those who are at high risk. OBJECTIVE: Predicted age difference (PAD) was proposed to predict CDR change. METHODS: All diffusion magnetic resonance imaging and CDR scores were obtained from the OASIS-3 databank. A brain age model was trained by a machine learning algorithm using the imaging data of 258 cognitively healthy adults. Two diffusion indices, i.e., mean diffusivity and fractional anisotropy, over the whole brain white matter were extracted to serve as the features for model training. The validated brain age model was applied to a longitudinal cohort of 217 participants who had CDR = 0 (CDR0), 0.5 (CDR0.5), and 1 (CDR1) at baseline. Participants were grouped according to different baseline CDR and their subsequent CDR in approximately 2 years of follow-up. PAD was compared between different groups with multiple comparison correction. RESULTS: PADs were significantly different among participants with different baseline CDRs. PAD in participants with relatively stable CDR0.5 was significantly smaller than PAD in participants who had CDR0.5 at baseline but converted to CDR1 in the follow-up. Similarly, participants with relatively stable CDR0 had significantly smaller PAD than those who were CDR0 at baseline but converted to CDR0.5 in the follow-up. CONCLUSION: Our results imply that PAD might be a potential imaging biomarker for predicting CDR outcomes in patients with CDR0 or CDR0.5.

Apathy is associated with white matter network disruption and specific cognitive deficits in Parkinson's disease.

BACKGROUND: Apathy is common in Parkinson's disease (PD) but its underlying white matter (WM) architecture is not well understood. Moreover, how apathy affects cognitive functions in PD remains unclear. We investigated apathy-related WM network alterations and the impact of apathy on cognition in the context of PD. METHODS: Apathetic PD patients (aPD), non-apathetic PD patients (naPD), and matched healthy controls (HCs) underwent brain scans and clinical assessment. Graph-theoretical and network-based analyses were used for group comparisons of WM features derived from diffusion spectrum imaging (DSI). Path analysis was used to determine the direct and indirect effects of apathy and other correlates on different cognitive functions. RESULTS: The aPD group was impaired on neural integration measured by global efficiency (p = 0.009) and characteristic path length (p = 0.04), executive function (p < 0.001), episodic memory (p < 0.001) and visuospatial ability (p = 0.02), and had reduced connectivity between the bilateral parietal lobes and between the putamen and temporal regions (p < 0.05). In PD, executive function was directly impacted by apathy and motor severity and indirectly influenced by depression; episodic memory was directly and indirectly impacted by apathy and depression, respectively; conversely, visuospatial ability was not related to any of these factors. Neural integration, though being marginally correlated with apathy, was not associated with cognition. CONCLUSIONS: Our results suggest compromised neural integration and reduced structural connectivity in aPD. Apathy, depression, and motor severity showed distinct impacts on different cognitive functions with apathy being the most influential determinant of cognition in PD.

Shared intrinsic functional connectivity alterations as a familial risk marker for ADHD: a resting-state functional magnetic resonance imaging study with sibling design.

BACKGROUND: Although aberrant intrinsic functional connectivity has been reported in attention-deficit/hyperactivity disorder (ADHD), we have a limited understanding of whether connectivity alterations are related to the familial risk of ADHD. METHODS: Fifty-three probands with ADHD, their unaffected siblings (n = 53) and typically developing controls (n = 53) underwent resting-state functional magnetic resonance imaging scans. A seed-based approach with the bilateral precuneus/posterior cingulate cortex (PCC) was used to derive a whole-brain functional connectivity map in each subject. The differences in functional connectivity among the three groups were tested with one-way ANOVA using randomized permutation. Comparisons between two groups were also performed to examine the increase or decrease in connectivity. The severity of ADHD symptoms was used to identify brain regions where symptom severity is correlated to the strength of intrinsic functional connectivity. RESULTS: When compared to controls, both probands and unaffected siblings showed increased functional connectivity in the left insula and left inferior frontal gyrus. The connectivity in these regions was linked to better performance in response inhibition in the control group but absent in other groups. Higher ADHD symptom severity was correlated with increased functional connectivity in bilateral fronto-parietal-temporal regions only noted in probands with ADHD. CONCLUSIONS: Alterations in resting-state functional connectivities with the precuneus/PCC, hubs of default-mode network, account for the underlying familial risks of ADHD. Since the left insula and left inferior frontal gyri are key regions of the salience and frontoparietal network, respectively, future studies focusing on alterations of cross-network functional connectivity as the familial risk of ADHD are suggested.

Neural substrates underpinning intra-individual variability in children with ADHD: A voxel-based morphometry study.

BACKGROUND/PURPOSE: Increased intra-individual variability (IIV) in reaction time (RT) is a key feature of attention-deficit/hyperactivity disorder (ADHD). However, little is known about neurobiology underpinnings of IIV in ADHD. METHODS: We assessed 55 youths with ADHD, and 55 individually-matched typically developing control (TDC) with the MRI and Conners' Continuous Performance Test. The ex-Gaussian distribution of RT was estimated to capture IIV with the parameters σ (sigma) and τ (tau). The regional brain volumes, analyzed by voxel-based morphometry, were correlated with IIV parameters. RESULTS: We found both distinct and shared correlations among ADHD and TDC. For grey matter, there were significant σ-by-group interactions in the cingulate cortex and thalamus and also a τ-by-group interaction in the right inferior frontal gyrus. There was also shared negative associations between σ and regional volumes of the right posterior cerebellum and a positive association between τ and the right anterior insula. For white matter, there was a significant σ-by-group interaction in the genu of the corpus callosum and significant τ-by-group interactions in the right anterior corona radiata, the left splenium of the corpus callosum, and bilateral posterior cerebellum. There were also shared patterns that increased τ was associated with increased regional volumes of the right anterior corona radiata and decreased regional volumes of the right posterior limb of the internal capsule. CONCLUSION: This study highlights that brain regions responsible for the motor, salience processing and multimodal information integration are associated with increased IIV in youths with ADHD.

Recent advances in using diffusion tensor imaging to study white matter alterations in Parkinson's disease: A mini review.

Parkinson's disease (PD) is the second most common age-related neurodegenerative disease with cardinal motor symptoms. In addition to motor symptoms, PD is a heterogeneous disease accompanied by many non-motor symptoms that dominate the clinical manifestations in different stages or subtypes of PD, such as cognitive impairments. The heterogeneity of PD suggests widespread brain structural changes, and axonal involvement appears to be critical to the pathophysiology of PD. As α-synuclein pathology has been suggested to cause axonal changes followed by neuronal degeneration, diffusion tensor imaging (DTI) as an in vivo imaging technique emerges to characterize early detectable white matter changes due to PD. Here, we reviewed the past 5-year literature to show how DTI has helped identify axonal abnormalities at different PD stages or in different PD subtypes and atypical parkinsonism. We also showed the recent clinical utilities of DTI tractography in interventional treatments such as deep brain stimulation (DBS). Mounting evidence supported by multisite DTI data suggests that DTI along with the advanced analytic methods, can delineate dynamic pathophysiological processes from the early to late PD stages and differentiate distinct structural networks affected in PD and other parkinsonism syndromes. It indicates that DTI, along with recent advanced analytic methods, can assist future interventional studies in optimizing treatments for PD patients with different clinical conditions and risk profiles.

White matter network disruption and cognitive correlates underlying impaired memory awareness in mild cognitive impairment.

Decreased awareness of memory declines in mild cognitive impairment (MCI) has been linked to structural or functional changes in a wide gray matter network; however, the underlying white matter pathway correlations for the memory awareness deficits remain unknown. Moreover, consistent findings have not been obtained regarding the cognitive basis of disturbed awareness of memory declines in MCI. Due to the methodological drawbacks (e.g., correlational analysis without controlling confounders related to clinical status, a problem related to the representativeness of the control group) of previous studies on the aforementioned topic, further investigation is required. To addressed the research gaps, this study investigated white matter microstructural integrity and the cognitive correlates of memory awareness in 87 older adults with or without mild cognitive impairment (MCI). The patients with MCI and healthy controls (HCs) were divided into two subgroups, namely those with normal awareness (NA) and poor awareness (PA) for memory deficit, according to the discrepancy scores calculated from the differences between subjective and objective memory evaluations. Only the results for HCs with NA (HC-NA) were compared with those for the two MCI groups (i.e., MCI-NA and MCI-PA). The three groups were matched on demographic and clinical variables. An advanced diffusion imaging technique-diffusion spectrum imaging-was used to investigate the integrity of the white matter tract. The results revealed that although the HC-NA group outperformed the two MCI groups on several cognitive tests, the two MCI groups exhibited comparable performance across different neuropsychological tests, except for the test on reasoning ability. Compared with the other two groups, the MCI-PA group exhibited lower integrity in bilateral frontal-striatal fibers, left anterior thalamocortical radiations, and callosal fibers connecting bilateral inferior parietal regions. These results could not be explained by gray matter morphometric differences. Overall, the results indicated that mnemonic anosognosia was not sufficient to explain the memory awareness deficits observed in the patients with MCI. Our brain imaging findings also support the concept of anosognosia for memory deficit as a disconnection syndrome in MCI.