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Abstract Network controllability refers to the ability to steer the state of a network towards a target state by driving certain nodes, known as input nodes. This concept can be applied to brain networks for studying brain function and its relation to the structure, which has numerous practical applications. Brain network controllability involves using external signals such as electrical stimulation to drive specific brain regions and navigate the neurophysiological activity level of the brain around the state space. Although controllability is mainly theoretical, the energy required for control is critical in real-world implementations. With a focus on the structural brain networks, this study explores the impact of white matter fiber architecture on the control energy in brain networks using the theory of how input node placement affects the LCC (the longest distance between inputs and other network nodes). Initially, we use a single input node as it is theoretically possible to control brain networks with just one input. We show that highly connected brain regions that lead to lower LCCs are more energy-efficient as a single input node. However, there may still be a need for a significant amount of control energy with one input, and achieving controllability with less energy could be of interest. We identify the minimum number of input nodes required to control brain networks with smaller LCCs, demonstrating that reducing the LCC can significantly decrease the control energy in brain networks. Our results show that relying solely on highly connected nodes is not effective in controlling brain networks with lower energy by using multiple inputs because of densely interconnected brain network hubs. Instead, a combination of low and high-degree nodes is necessary.

Neuroanatomical abnormalities have been reported along a continuum from at-risk stages, including high schizotypy, to early and chronic psychosis. However, a comprehensive neuroanatomical mapping of schizotypy remains to be established. The authors conducted the first large-scale meta-analyses of cortical and subcortical morphometric patterns of schizotypy in healthy individuals, and compared these patterns with neuroanatomical abnormalities observed in major psychiatric disorders. The sample comprised 3004 unmedicated healthy individuals (12-68 years, 46.5% male) from 29 cohorts of the worldwide ENIGMA Schizotypy working group. Cortical and subcortical effect size maps with schizotypy scores were generated using standardized methods. Pattern similarities were assessed between the schizotypy-related cortical and subcortical maps and effect size maps from comparisons of schizophrenia (SZ), bipolar disorder (BD) and major depression (MDD) patients with controls. Thicker right medial orbitofrontal/ventromedial prefrontal cortex (mOFC/vmPFC) was associated with higher schizotypy scores (r = 0.067, pFDR = 0.02). The cortical thickness profile in schizotypy was positively correlated with cortical abnormalities in SZ (r = 0.285, pspin = 0.024), but not BD (r = 0.166, pspin = 0.205) or MDD (r = -0.274, pspin = 0.073). The schizotypy-related subcortical volume pattern was negatively correlated with subcortical abnormalities in SZ (rho = -0.690, pspin = 0.006), BD (rho = -0.672, pspin = 0.009), and MDD (rho = -0.692, pspin = 0.004). Comprehensive mapping of schizotypy-related brain morphometry in the general population revealed a significant relationship between higher schizotypy and thicker mOFC/vmPFC, in the absence of confounding effects due to antipsychotic medication or disease chronicity. The cortical pattern similarity between schizotypy and schizophrenia yields new insights into a dimensional neurobiological continuity across the extended psychosis phenotype.

Abstract The drivers of cognitive change following first-episode psychosis remain poorly understood. Evidence regarding the role of antipsychotic medication is primarily based on naturalistic studies or clinical trials without a placebo arm, making it difficult to disentangle illness from medication effects. A secondary analysis of a randomised, triple-blind, placebo-controlled trial, where antipsychotic-naive patients with first-episode psychotic disorder were allocated to receive risperidone/paliperidone or matched placebo plus intensive psychosocial therapy for 6 months was conducted. A healthy control group was also recruited. A cognitive battery was administered at baseline and 6 months. Intention-to-treat analysis involved 76 patients (antipsychotic medication group: 37; 18.6Mage [2.9] years; 21 women; placebo group: 39; 18.3Mage [2.7]; 22 women); and 42 healthy controls (19.2Mage [3.0] years; 28 women). Cognitive performance predominantly remained stable (working memory, verbal fluency) or improved (attention, processing speed, cognitive control), with no group-by-time interaction evident. However, a significant group-by-time interaction was observed for immediate recall (p = 0.023), verbal learning (p = 0.024) and delayed recall (p = 0.005). The medication group declined whereas the placebo group improved on each measure (immediate recall: p = 0.024; η p 2 = 0.062; verbal learning: p = 0.015; η p 2 = 0.072 both medium effects; delayed recall: p = 0.001; η p 2 = 0.123 large effect). The rate of change for the placebo and healthy control groups was similar. Per protocol analysis (placebo n = 16, medication n = 11) produced similar findings. Risperidone/paliperidone may worsen verbal learning and memory in the early months of psychosis treatment. Replication of this finding and examination of various antipsychotic agents are needed in confirmatory trials. Antipsychotic effects should be considered in longitudinal studies of cognition in psychosis. Trial registration: Australian New Zealand Clinical Trials Registry ( http://www.anzctr.org.au/ ; ACTRN12607000608460).

Abstract Both psychotic illness and subclinical psychosis-like experiences (PLEs) have been associated with cortico-striatal dysfunction. This work has largely relied on a discrete parcellation of the striatum into distinct functional areas, but recent evidence suggests that the striatum comprises multiple overlapping and smoothly varying gradients (i.e., modes) of functional organization. Here, we investigated two of these functional connectivity modes, previously associated with variations in the topographic patterning of cortico-striatal connectivity (first-order gradient), and dopaminergic innervation of the striatum (second-order gradient), and assessed continuities in striatal function from subclinical to clinical domains. We applied connectopic mapping to resting-state fMRI data to obtain the first-order and second-order striatal connectivity modes in two distinct samples: (1) 56 antipsychotic-free patients (26 females) with first-episode psychosis (FEP) and 27 healthy controls (17 females); and (2) a community-based cohort of 377 healthy individuals (213 females) comprehensively assessed for subclinical PLEs and schizotypy. The first-order “cortico-striatal” and second-order “dopaminergic” connectivity gradients were significantly different in FEP patients compared to controls bilaterally. In the independent sample of healthy individuals, variations in the left first-order “cortico-striatal” connectivity gradient were associated with inter-individual differences in a factor capturing general schizotypy and PLE severity. The presumed cortico-striatal connectivity gradient was implicated in both subclinical and clinical cohorts, suggesting that variations in its organization may represent a neurobiological trait marker across the psychosis continuum. Disruption of the presumed dopaminergic gradient was only noticeable in patients, suggesting that neurotransmitter dysfunction may be more apparent to clinical illness.

The clinical heterogeneity of autism spectrum disorders majorly challenges their genetic study. Autism spectrum disorders symptoms occur in milder forms in the general population, as autistic-like traits, and share genetic factors with autism spectrum disorders. Here, we investigate the genetics of individual autistic-like traits to improve our understanding of autism spectrum disorders. We meta-analysed four population-based genome-wide association studies investigating four autistic-like traits -- 'attention-to-detail', 'imagination', 'rigidity' and 'social-skills' (n = 4600). Using autism spectrum disorder summary statistics from the Psychiatric Genomic Consortium (N = 46,350), we applied polygenic risk score analyses to understand the genetic relationship between autism spectrum disorders and autistic-like traits. Using MAGMA, we performed gene-based and gene co-expression network analyses to delineate involved genes and pathways. We identified two novel genome-wide significant loci -- rs6125844 and rs3731197 -- associated with 'attention-to-detail'. We demonstrated shared genetic aetiology between autism spectrum disorders and 'rigidity'. Analysing top variants and genes, we demonstrated a role of the immune-related genes "RNF114," "CDKN2A," "KAZN," "SPATA2" and "ZNF816A" in autistic-like traits. Brain-based genetic expression analyses further linked autistic-like traits to genes involved in immune functioning, and neuronal and synaptic signalling. Overall, our findings highlight the potential of the autistic-like trait-based approach to address the challenges of genetic research in autism spectrum disorders. We provide novel insights showing a potential role of the immune system in specific autism spectrum disorder dimensions.

Background: The finding that transcranial magnetic stimulation (TMS) can enhance memory performance via stimulation of parietal sites within the Cortical-Hippocampal Network counts as one of the most exciting findings in this field in the past decade. However, the first independent effort aiming to fully replicate this finding found no discernible influence of TMS on memory performance. Objective: We examined whether this might relate to interindividual spatial variation in brain connectivity architecture, and the capacity of personalisation methodologies to overcome the noise inherent across independent scanners and cohorts. Methods: We implemented recently detailed personalisation methodology to retrospectively compute individual-specific parietal targets and then examined relation to TMS outcomes. Results: Closer proximity between actual and novel fMRI-personalized targets associated with greater improvement in memory performance. Conclusion: These findings demonstrate the potential importance of aligning brain stimulation targets according to individual-specific differences in brain connectivity, and extend upon recent findings in prefrontal cortex.

In adulthood, stress exposure and genetic risk heighten psychological vulnerability by accelerating neurobiological senescence. To investigate whether molecular and brain network maturation processes play a similar role in adolescence, we analysed genetic, as well as longitudinal task neuroimaging (inhibitory control, incentive processing) and early life adversity (i.e., material deprivation, violence) data from the Adolescent Brain and Cognitive Development study (N = 980, age range: 9–13 years). Genetic risk was estimated separately for Major Depressive Disorder (MDD) and Alzheimer’s Disease (AD), two pathologies linked to stress exposure and allegedly sharing a causal connection (MDD-to-AD). Adversity and genetic risk for MDD/AD jointly predicted functional network segregation patterns suggestive of accelerated (GABA-linked) visual/attentional, but delayed (dopamine [D2]/glutamate [GLU5R]-linked) somatomotor/association system development. A positive relationship between brain maturation and psychopathology emerged only among the less vulnerable adolescents, thereby implying that normatively maladaptive neurodevelopmental alterations could foster adjustment among the more exposed and genetically more stress susceptible youths. Transcriptomic analyses suggested that sensitivity to stress may underpin the joint neurodevelopmental effect of adversity and genetic risk for MDD/AD, in line with the proposed role of negative emotionality as a precursor to AD, likely to account for the alleged causal impact of MDD on dementia onset.

Abstract Despite 50+ years of drug discovery, current antipsychotics have limited efficacy against negative and cognitive symptoms of schizophrenia, and are ineffective—with the exception of clozapine—against any symptom domain for patients who are treatment resistant. Novel therapeutics with diverse non-dopamine D2 receptor targets have been explored extensively in clinical trials, yet often fail due to a lack of efficacy despite showing promise in preclinical development. This lack of translation between preclinical and clinical efficacy suggests a systematic failure in current methods that determine efficacy in preclinical rodent models. In this review, we critically evaluate rodent models and behavioural tests used to determine preclinical efficacy, and look to clinical research to provide a roadmap for developing improved translational measures. We highlight the dependence of preclinical models and tests on dopamine-centric theories of dysfunction and how this has contributed towards a self-reinforcing loop away from clinically meaningful predictions of efficacy. We review recent clinical findings of distinct dopamine-mediated dysfunction of corticostriatal circuits in patients with treatment-resistant vs. non-treatment-resistant schizophrenia and suggest criteria for establishing rodent models to reflect such differences, with a focus on objective, translational measures. Finally, we review current schizophrenia drug discovery and propose a framework where preclinical models are validated against objective, clinically informed measures and preclinical tests of efficacy map onto those used clinically.

Measurement(s) brain activity Technology Type(s) functional magnetic resonance imaging • FDG-Positron Emission Tomography Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14977851

Asymmetries of the cerebral cortex are found across diverse phyla and are particularly pronounced in humans, with important implications for brain function and disease. However, many prior studies have confounded asymmetries due to size with those due to shape. Here, we introduce a novel approach to characterize asymmetries of the whole cortical shape, independent of size, across different spatial frequencies using magnetic resonance imaging data in three independent datasets. We find that cortical shape asymmetry is highly individualized and robust, akin to a cortical fingerprint, and identifies individuals more accurately than size-based descriptors, such as cortical thickness and surface area, or measures of inter-regional functional coupling of brain activity. Individual identifiability is optimal at coarse spatial scales (~37 mm wavelength), and shape asymmetries show scale-specific associations with sex and cognition, but not handedness. While unihemispheric cortical shape shows significant heritability at coarse scales (~65 mm wavelength), shape asymmetries are determined primarily by subject-specific environmental effects. Thus, coarse-scale shape asymmetries are highly personalized, sexually dimorphic, linked to individual differences in cognition, and are primarily driven by stochastic environmental influences.

Abstract Background ASD and ADHD are prevalent neurodevelopmental disorders that frequently co-occur and have strong evidence for a degree of shared genetic aetiology. Behavioural and neurocognitive heterogeneity in ASD and ADHD has hampered attempts to map the underlying genetics and neurobiology, predict intervention response, and improve diagnostic accuracy. Moving away from categorical conceptualisations of psychopathology to a dimensional approach is anticipated to facilitate discovery of data-driven clusters and enhance our understanding of the neurobiological and genetic aetiology of these conditions. The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project is one of the first large-scale, family-based studies to take a truly transdiagnostic approach to ASD and ADHD. Using a comprehensive phenotyping protocol capturing dimensional traits central to ASD and ADHD, the MAGNET project aims to identify data-driven clusters across ADHD-ASD spectra using deep phenotyping of symptoms and behaviours; investigate the degree of familiality for different dimensional ASD-ADHD phenotypes and clusters; and map the neurocognitive, brain imaging, and genetic correlates of these data-driven symptom-based clusters. Methods The MAGNET project will recruit 1,200 families with children who are either typically developing, or who display elevated ASD, ADHD, or ASD-ADHD traits, in addition to affected and unaffected biological siblings of probands, and parents. All children will be comprehensively phenotyped for behavioural symptoms, comorbidities, neurocognitive and neuroimaging traits and genetics. Conclusion The MAGNET project will be the first large-scale family study to take a transdiagnostic approach to ASD-ADHD, utilising deep phenotyping across behavioural, neurocognitive, brain imaging and genetic measures.

How genes sculpt the complex architecture of the human connectome remains unclear. Here, the authors show that genes preferentially influence the strength of connectivity between functionally valuable, metabolically costly connections between brain network hubs.