Common and distinct cortical thickness alterations in youth with autism spectrum disorder and attention-deficit/hyperactivity disorder

Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis

Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis is warranted to identify the most robust findings and imaging modalities, and to compare predictive outcomes obtained in magnetic resonance imaging (MRI) and studies using clinical and demographic features. We conducted a literature search from database inception to July 22, 2023, to identify studies using pretreatment clinical or brain MRI features to predict treatment outcomes in patients with MDD. Two meta-analyses were conducted on clinical and MRI studies, respectively. The meta-regression was employed to explore the effects of covariates and compare the predictive performance between clinical and MRI groups, as well as across MRI modalities and intervention subgroups. Meta-analysis of 13 clinical studies yielded an area under the curve (AUC) of 0.73, while in 44 MRI studies, the AUC was 0.89. MRI studies showed a higher sensitivity than clinical studies (0.78 vs. 0.62, Z = 3.42, P = 0.001). In MRI studies, resting-state functional MRI (rsfMRI) exhibited a higher specificity than task-based fMRI (tbfMRI) (0.79 vs. 0.69, Z = -2.86, P = 0.004). No significant differences in predictive performance were found between structural and functional MRI, nor between different interventions. Of note, predictive MRI features for treatment outcomes in studies using antidepressants were predominantly located in the limbic and default mode networks, while studies of electroconvulsive therapy (ECT) were restricted mainly to the limbic network. Our findings suggest a promise for pretreatment brain MRI features to predict MDD treatment outcomes, outperforming clinical features. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. Overlapping but distinct network-level measures predicted antidepressants and ECT outcomes. Future studies are needed to predict outcomes using multiple MRI features, and to clarify whether imaging features predict outcomes generally or differ depending on treatments.

Neural mechanisms of non-pharmacological interventions in patients with mild cognitive impairment and Alzheimer's disease: An ALE meta-analysis

Non-pharmacologic interventions are effective for persons showing mild cognitive impairment (MCI) and Alzheimer's disease (AD). We used activation likelihood estimation (ALE) meta-analysis to systematically quantify the results of 19 neuroimaging studies in order to identify brain regions in which patients showed stable increases or decreases in activation after interventions. We also tested the moderating effects of disease stage (MCI vs. AD) and intervention modality (cognitive training vs. exercise intervention). The results showed increased activation in the cuneus, precuneus and medial frontal gyrus in the combined groups after treatment, whereas the anterior cingulate gyrus showed decreased activation. Secondly, in the MCI group there was increased activation in the precuneus and precentral gyrus after treatment, whereas there was decreased activation in the anterior cingulate gyrus; in the AD group there was only increased activation after treatment, including in the lingual gyrus and bilateral superior temporal gyrus. Finally, the bilateral cuneus and precentral gyrus showed increased activation after cognitive training, while bilateral insula, among others, showed decreased activation. This suggests that there are brain activation changes after non-pharmacological treatments for MCI and AD patients, but that the treatment mechanisms are moderated by stage and intervention modality. Future studies could continue to explore specific neural mechanisms involved in different intervention conditions for these patients.

Neural Activity Alterations and Their Association With Neurotransmitter and Genetic Profiles in Schizophrenia: Evidence From Clinical Patients and Unaffected Relatives

BACKGROUND

The pattern of abnormal resting-state brain function has been documented in schizophrenia (SCZ). However, as of yet, it remains unclear whether this pattern is of genetic predisposition or related to the illness itself.

METHODS

A systematical meta-analysis was performed to identify resting-state functional differences in probands and their high-risk first-degree relatives of schizophrenia (FDRs-SCZ) using Seed-based d Mapping software. Subsequently, spatial associations between postmortem gene expression and neurotransmitters distribution data and neural activity alterations were conducted to uncover neural mechanisms underlaying FDRs-SCZ and SCZ from a multidimensional perspective.

RESULTS

A total of 13 studies comprising 503 FDRs-SCZ and 605 healthy controls (HCs) and 129 studies comprising 6506 patients with SCZ and 6982 HCs were included. Compared to HCs, FDRs-SCZ displayed increased spontaneous functional activity in the bilateral anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC); patients with SCZ showed decreased spontaneous functional activity in the bilateral ACC/mPFC, bilateral postcentral gyrus, and right middle temporal gyrus as well as increased spontaneous functional activity in the bilateral striatum. The altered functional activity in FDRs-SCZ and SCZ shared similar spatial associations with genes enriched in potassium ion transmembrane transport, channel activity, and complex. The FDRs-SCZ and SCZ-related brain functional patterns were additionally associated with dopaminergic, serotonergic, and cholinergic neurotransmitter distribution.

CONCLUSIONS

SCZ-related resting-state functional, neuroimaging transcriptomes, and neurotransmitters abnormalities may exist in high-risk unaffected FDRs-SCZ, rather than just in overt SCZ. The study extended the evidence that altered brain function, along with their spatial correlations to genetics and neurotransmitter systems, may associate with genetic vulnerability for SCZ.

A multimodal neuroimaging meta-analysis of functional and structural brain abnormalities in attention-deficit/hyperactivity disorder

BACKGROUND

Numerous neuroimaging studies utilizing resting-state functional imaging and voxel-based morphometry (VBM) have identified variations in distinct brain regions among individuals with attention-deficit/hyperactivity disorder (ADHD). However, the results have been inconsistent.

METHODS

A comprehensive voxel-wise meta-analysis was performed on studies employing resting-state functional imaging and gray matter volume (GMV), examining discrepancies between individuals with ADHD and neurotypical controls (NCs). The analysis utilized the Seed-based d Mapping software.

RESULTS

A systematic review of the literature identified 21 functional imaging studies (595 ADHD and 564 controls) and 50 GMV studies (1907 ADHD and 1611 controls). In general, individuals with ADHD exhibited increased resting-state functional activity in the right parahippocampal gyrus and bilateral orbitofrontal cortex (OFC), as well as decreased resting-state functional activity in the bilateral cingulate cortex (including the posterior cingulate cortex [PCC], median cingulate cortex [MCC], and anterior cingulate cortex [ACC]). The VBM meta-analysis revealed decreased GMV in the bilateral OFC, right putamen (extending to right superior temporal gyrus [STG]), left inferior frontal gyrus (IFG), right superior frontal gyrus (SFG), ACC, and precentral gyrus among individuals with ADHD.

CONCLUSIONS

The multimodal meta-analyses indicated that individuals with ADHD exhibit abnormalities in both function and structure in the bilateral OFC. In addition, a few regions exhibited only functional or only structural abnormalities in ADHD, such as in the limbic, prefrontal, primary sensorimotor regions.

MQGA: A quantitative analysis of brain network hubs using multi-graph theoretical indices

Recent advancements in large-scale network studies have shown that connector hubs and provincial hubs are vital for coordinating complex cognitive tasks by facilitating information transfer between and within specialized modules. However, current methods for identifying these hubs often lack standardized measurement criteria, hindering quantitative analysis. This study proposes a novel computational method utilizing multi-graph theoretical index calculations to quantitatively analyze hub attributes in brain networks. Using benchmark network, random simulation network (N = 100), resting fMRI data from the ADHD-200 NYU dataset (HC = 110, ADHD = 146), and the Peking dataset (HC = 120, ADHD = 83), we introduce the Multi-criteria Quantitative Graph Analysis (MQGA) method, which employs betweenness centrality, degree centrality, and participation coefficient to determine the connector (con) hub index and provincial (pro) hub index. The method's accuracy, reliability, and stability were validated through correlation analysis of hub indices and labels, vulnerability tests, and consistency analysis across subjects. Results indicate that as network sparsity increases, the con hub index increases while the pro hub index decreases, with the optimal hub node index at 4 % sparsity. Vulnerability tests revealed that removing con nodes had a greater impact on network integrity than removing pro nodes. Both con and pro exhibited stability in consistency analyses, but con was more stable. The stability of hub scores in disease groups was significantly lower than in the healthy control group. High con values were found in the precuneus, postcentral gyrus, and precentral gyrus, whereas high pro values were identified in the precentral gyrus, postcentral gyrus, superior parietal lobule, precuneus, and superior temporal gyrus. This approach enhances the accuracy and sensitivity of hub node identification, facilitating precise comparisons and producing consistent, replicable results, advancing our understanding of brain network hub nodes, their roles in cognitive processes, and their implications for brain disease research.

Combination of motor, sensory and affective tasks in an EEG paradigm for children with developmental disabilities

Individuals with neurodevelopmental disorders exhibit overlapping emotional, somatosensory and motor deficits. Although brain processes underlying these impairments have been extensively studied in a separate way, the brain interaction of these inputs is an innovative line of research. Here we present a new EEG methodology for exploring the interactive brain activity of sensorimotor and affective stimuli. The task consists in presenting affective stimuli of different modalities (e.g. affective pictures, affective touch) while simultaneously an arthromotor performs passive joint movements, unseen by the participant. Participants were then required to press one of two buttons to indicate if their joint position agreed with a picture shown in a screen. Pilot data of electroencephalography recordings revealed distinct somatosensory event-related potentials (SEP) when movement was subsequent to affective stimuli, compared to neutral stimuli, as well as a differentiation of SEPs for different neurodevelopmental conditions. Behavioral responses further showed that children with cerebral palsy had more errors to identify their hand position when they were exposed to affective stimuli. This paradigm is a valuable tool to explore the modulative influence of emotion in the sensorimotor brain processing of different populations with joint emotional and sensorimotor impairments, such as children with neurodevelopmental disorders or patients with stroke.•This method allows exploring the interaction between affective and sensoriomotor inputs in an EEG paradigm.

Task-based modulation of functional connectivity of dorsal attention network in adult-ADHD

Recent studies have prompted a shift in the understanding of attention deficit hyperactivity disorder (ADHD) from models positing dysfunction of individual brain areas to those that assume alterations in large-scale brain networks. Despite this shift, the underlying neural mechanism of ADHD in the adult population remains uncertain. With functional magnetic resonance imaging (fMRI), this study examined brain connectivity of dorsal and ventral attention networks. Adults with and without ADHD completed a Go/No-Go task inside the scanner and the functional connectivity of attention networks was analysed. The generalized psychophysiological interaction analysis indicated differences involving the dorsal attention network. For the ADHD group, an interaction effect revealed altered dorsal attention-default mode network connectivity modulation, particularly between the right frontal eye field and posterior cingulate gyrus. We conclude that dorsal attention network dysfunction may be involved in sustained attention deficits in adult-ADHD. This study sheds light into network-level alterations contributing to the understanding of adult-ADHD, which may be a potential avenue for future research and clinical interventions.

Unsupervised machine learning for identifying attention-deficit/hyperactivity disorder subtypes based on cognitive function and their implications for brain structure

BACKGROUND

Structural anomalies in the frontal lobe and basal ganglia have been reported in patients with attention-deficit/hyperactivity disorder (ADHD). However, these findings have been not always consistent because of ADHD diversity. This study aimed to identify ADHD subtypes based on cognitive function and find their distinct brain structural characteristics.

METHODS

Using the data of 656 children with ADHD from the Adolescent Brain Cognitive Development (ABCD) Study, we applied unsupervised machine learning to identify ADHD subtypes using the National Institutes of Health Toolbox Tasks. Moreover, we compared the regional brain volumes between each ADHD subtype and 6601 children without ADHD (non-ADHD).

RESULTS

Hierarchical cluster analysis automatically classified ADHD into three distinct subtypes: ADHD-A (n = 212, characterized by high-order cognitive ability), ADHD-B (n = 190, characterized by low cognitive control, processing speed, and episodic memory), and ADHD-C (n = 254, characterized by strikingly low cognitive control, working memory, episodic memory, and language ability). Structural analyses revealed that the ADHD-C type had significantly smaller volumes of the left inferior temporal gyrus and right lateral orbitofrontal cortex than the non-ADHD group, and the right lateral orbitofrontal cortex volume was positively correlated with language performance in the ADHD-C type. However, the volumes of the ADHD-A and ADHD-B types were not significantly different from those of the non-ADHD group.

CONCLUSIONS

These results indicate the presence of anomalies in the lateral orbitofrontal cortex associated with language deficits in the ADHD-C type. Subtype specificity may explain previous inconsistencies in brain structural anomalies reported in ADHD.

Linked patterns of symptoms and cognitive covariation with functional brain controllability in major depressive disorder

BACKGROUND

Controllability analysis is an approach developed for evaluating the ability of a brain region to modulate function in other regions, which has been found to be altered in major depressive disorder (MDD). Both depressive symptoms and cognitive impairments are prominent features of MDD, but the case-control differences of controllability between MDD and controls can not fully interpret the contribution of both clinical symptoms and cognition to brain controllability and linked patterns among them in MDD.

METHODS

Sparse canonical correlation analysis was used to investigate the associations between resting-state functional brain controllability at the network level and clinical symptoms and cognition in 99 first-episode medication-naïve patients with MDD.

FINDINGS

Average controllability was significantly correlated with clinical features. The average controllability of the dorsal attention network (DAN) and visual network had the highest correlations with clinical variables. Among clinical variables, depressed mood, suicidal ideation and behaviour, impaired work and activities, and gastrointestinal symptoms were significantly negatively associated with average controllability, and reduced cognitive flexibility was associated with reduced average controllability.

INTERPRETATION

These findings highlight the importance of brain regions in modulating activity across brain networks in MDD, given their associations with symptoms and cognitive impairments observed in our study. Disrupted control of brain reconfiguration of DAN and visual network during their state transitions may represent a core brain mechanism for the behavioural impairments observed in MDD.

FUNDING

National Natural Science Foundation of China (82001795 and 82027808), National Key R&D Program (2022YFC2009900), and Sichuan Science and Technology Program (2024NSFSC0653).

Correlations between developmental trajectories of brain functional connectivity, neurocognitive functions, and clinical symptoms in patients with attention-deficit hyperactivity disorder

Several studies on attention-deficit hyperactivity disorder (ADHD) have suggested a developmental sequence of brain changes: subcortico-subcortical connectivity in children, evolving to subcortico-cortical in adolescence, and culminating in cortico-cortical connectivity in young adulthood. This study hypothesized that children with ADHD would exhibit decreased functional connectivity (FC) between the cortex and striatum compared to adults with ADHD, who may show increased FC in these regions. Seventy-six patients with ADHD (26 children, 26 adolescents, and 24 adults) and 74 healthy controls (25 children, 24 adolescents, and 25 adults) participated in the study. Resting state magnetic resonance images were acquired using a 3.0 T Philips Achieva scanner. The results indicated a gradual decrease in the number of subcategories representing intelligence quotient deficits in the ADHD group with age. In adulthood, the ADHD group exhibited lower working memory compared to the healthy control group. The number of regions showing decreased FC from the cortex to striatum between the ADHD and control groups reduced with age, while regions with increased FC from the default mode network and attention network in the ADHD group increased with age. In adolescents and adults, working memory was positively associated with brain activity in the postcentral gyrus and negatively correlated with ADHD clinical symptoms. In conclusion, the findings suggest that intelligence deficits in certain IQ subcategories may diminish as individuals with ADHD age. Additionally, the study indicates an increasing anticorrelation between cortical and subcortical regions with age in individuals with ADHD.

Shared and differing functional connectivity abnormalities of the default mode network in mild cognitive impairment and Alzheimer's disease

Alzheimer's disease (AD) and mild cognitive impairment (MCI) both show abnormal resting-state functional connectivity (rsFC) of default mode network (DMN), but it is unclear to what extent these abnormalities are shared. Therefore, we performed a comprehensive meta-analysis, including 31 MCI studies and 20 AD studies. MCI patients, compared to controls, showed decreased within-DMN rsFC in bilateral medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC), precuneus/posterior cingulate cortex (PCC), right temporal lobes, and left angular gyrus and increased rsFC between DMN and left inferior temporal gyrus. AD patients, compared to controls, showed decreased rsFC within DMN in bilateral mPFC/ACC and precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC between DMN and right dorsolateral prefrontal cortex. Conjunction analysis showed shared decreased rsFC in mPFC/ACC and precuneus/PCC. Compared to MCI, AD had decreased rsFC in left precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC in right temporal lobes. MCI and AD share a decreased within-DMN rsFC likely underpinning episodic memory deficits and neuropsychiatric symptoms, but differ in DMN rsFC alterations likely related to impairments in other cognitive domains such as language, vision, and execution. This may throw light on neuropathological mechanisms in these two stages of dementia.