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Evidence from functional neuroimaging studies support neural differences between the Attention Deficit Hyperactivity Disorder (ADHD) presentation types. It remains unclear if these neural deficits also manifest at the structural level. We have previously shown that the ADHD combined, and ADHD inattentive types demonstrate differences in graph properties of structural covariance suggesting an underlying difference in neuroanatomical organization. The goal of this study was to examine and validate white matter brain organization between the two subtypes using both scalar and connectivity measures of brain white matter. We used both tract-based spatial statistical (TBSS) and tractography analyses with network-based Statistics (NBS) and graph-theoretical analyses in a cohort of 35 ADHD participants (aged 8-17 years) defined using DSM-IV criteria as combined (ADHD-C) type (n = 19) or as predominantly inattentive (ADHD-I) type (n = 16), and 28 matched neurotypical controls. We performed TBSS analyses on scalar measures of fractional anisotropy (FA), mean (MD), radial (RD), and axial (AD) diffusivity to assess differences in WM between ADHD types and controls. NBS and graph theoretical analysis of whole brain inter-regional tractography examined connectomic differences and brain network organization, respectively. None of the scalar measures significantly differed between ADHD types or relative to controls. Similarly, there were no tractography connectivity differences between the two subtypes and relative to controls using NBS. Global and regional graph measures were also similar between the groups. A single significant finding was observed for nodal degree between the ADHD-C and controls, in the right insula (corrected p = .029). Our result of no white matter differences between the subtypes is consistent with most previous findings. These findings together might suggest that the white matter structural architecture is largely similar between the DSM-based ADHD presentations is similar to the extent of being undetectable with the current cohort size.

Abstract Background Lisdexamfetamine dimesylate (LDX) has demonstrated safety and efficacy for treatment of Binge Eating Disorder (BED). However, to date, trials have not included participants with co-occurring psychiatric disorders. This study explores how LDX affects eating disorder psychopathology, symptoms of common psychiatric comorbidities of BED (ADHD, depression, anxiety), and psychological quality of life, in people with moderate to severe BED. Methods These are secondary analyses of an open-label LDX trial conducted in 41 adults (18–40 years) over eight-weeks. Participants received LDX titrated to 50 or 70 mg. Clinical assessments and self-report questionnaires were conducted at baseline and 8-week follow-up. Results Eating disorder psychopathology and psychological quality of life improved after 8-weeks of LDX. No significant group-level changes in depression, anxiety or ADHD severity scores were observed. However, the majority within the small subsets with elevated depression and ADHD symptoms experienced reduced depressive and inattentive symptom severity, respectively. Conclusions We provide proof-of-concept evidence that LDX may provide broader psychological benefits to individuals with BED, beyond reducing their BE frequency. Effects of LDX on anxiety should be monitored closely by clinicians. Early indications suggest that LDX may be effectively used in people with BED, with and without co-occurring psychiatric conditions, however tolerability may be lower in highly complex cases. Trial registration: Australian and New Zealand Clinical Trials Registry (anzctr.org.au) #ACTRN12618000623291.

The ability to learn contingencies between actions and outcomes in a dynamic environment is critical for flexible, adaptive behavior. Goal-directed actions adapt to changes in action-outcome contingencies as well as to changes in the reward-value of the outcome. When networks involved in reward processing and contingency learning are maladaptive, this fundamental ability can be lost, with detrimental consequences for decision-making. Impaired decision-making is a core feature in a number of psychiatric disorders, ranging from depression to schizophrenia. The argument can be developed, therefore, that seemingly disparate symptoms across psychiatric disorders can be explained by dysfunction within common decision-making circuitry. From this perspective, gaining a better understanding of the neural processes involved in goal-directed action, will allow a comparison of deficits observed across traditional diagnostic boundaries within a unified theoretical framework. This review describes the key processes and neural circuits involved in goal-directed decision-making using evidence from animal studies and human neuroimaging. Select studies are discussed to outline what we currently know about causal judgments regarding actions and their consequences, action-related reward evaluation, and, most importantly, how these processes are integrated in goal-directed learning and performance. Finally, we look at how adaptive decision-making is impaired across a range of psychiatric disorders and how deepening our understanding of this circuitry may offer insights into phenotypes and more targeted interventions.

IntroductionSelf-directed dieting (i.e., unsupervised) is very common among adolescents and young adults but has had almost no direct research. This paper describes the protocol for the My Diet Study, a two-arm observational investigation of the natural progression of dieting among young people over a period of 6-months. The study aims to examine the links between self-directed dieting, general physiological and psychological metrics of wellbeing (e.g., depressive symptoms) and biomarkers of gut-brain axis functions (e.g., microbiome and hormones) that are predicted to influence diet adherence through appetite, mood and metabolism regulation.MethodsYoung people aged 16–25, intending to start a diet will be invited to participate in this observational study. For Part 1 (psychological arm), participants will be asked to complete a set of questionnaires and diaries at the beginning of every month for 6 months, to assess overall mental (e.g., psychological distress, disordered eating) and physical (e.g., weight) health, perceived diet success, food intake and gastrointestinal movements. For Part 2 (biological arm), a subsample of 50 participants will be asked to provide feces, blood and saliva for bio-sampling each month for the first 3-months of their participation in Part 1.DiscussionThe My Diet Study will be the first longitudinal, observational study of dieting in young people combining in-depth psychological and biological data. It is anticipated that the findings will yield psychological & biological information about the impacts and effectiveness of self-directed dieting in young people, inform a framework for advice on safety in dieting among young people and help to establish the potential for biomarkers for risk management and improvement of diet-based lifestyle interventions.

Abstract Bipolar disorder (BD) is commonly misdiagnosed as major depressive disorder (MDD). This is understandable, as depression often precedes mania and is otherwise indistinguishable in both. It is therefore imperative to identify neural mechanisms that can differentiate the two disorders. Interrogating resting brain neural activity may reveal core distinguishing abnormalities. We adopted an a priori approach, examining three key networks documented in previous mood disorder literature subserving executive function, salience and rumination that may differentiate euthymic BD and MDD patients. Thirty-eight patients with BD, 39 patients with MDD matched for depression severity, and 39 age-gender matched healthy controls, completed resting-state fMRI scans. Seed-based and data-driven Independent Component analyses (ICA) were implemented to examine group differences in resting-state connectivity (pFDR

Abstract Behavioural disturbances in attention deficit hyperactivity disorder (ADHD) are thought to be due to dysfunction of spatially distributed, interconnected neural systems. While there is a fast-growing literature on functional dysconnectivity in ADHD, far less is known about the structural architecture underpinning these disturbances and how it may contribute to ADHD symptomology and treatment prognosis. We applied graph theoretical analyses on diffusion MRI tractography data to produce quantitative measures of global network organisation and local efficiency of network nodes. Support vector machines (SVMs) were used for comparison of multivariate graph measures of 37 children and adolescents with ADHD relative to 26 age and gender matched typically developing children (TDC). We also explored associations between graph measures and functionally-relevant outcomes such as symptom severity and prediction of methylphenidate (MPH) treatment response. We found that multivariate patterns of reduced local efficiency, predominantly in subcortical regions (SC), were able to distinguish between ADHD and TDC groups with 76% accuracy. For treatment prognosis, higher global efficiency, higher local efficiency of the right supramarginal gyrus and multivariate patterns of increased local efficiency across multiple networks at baseline also predicted greater symptom reduction after 6 weeks of MPH treatment. Our findings demonstrate that graph measures of structural topology provide valuable diagnostic and prognostic markers of ADHD, which may aid in mechanistic understanding of this complex disorder.

Neuroimaging studies have revealed neurobiological differences in ADHD, particularly studies examining connectivity disruption and anatomical network organization. However, the underlying pathophysiology of ADHD types remains elusive as it is unclear whether dysfunctional network connections characterize the underlying clinical symptoms distinguishing ADHD types. Here, we investigated intrinsic functional network connectivity to identify neural signatures that differentiate the combined (ADHD-C) and inattentive (ADHD-I) presentation types. Applying network-based statistical (NBS) and graph theoretical analysis to task-derived intrinsic connectivity data from completed fMRI scans, we evaluated default mode network (DMN) and whole-brain functional network topology in a cohort of 34 ADHD participants (aged 8–17 years) defined using DSM-IV criteria as predominantly inattentive (ADHD-I) type (n = 15) or combined (ADHD-C) type (n = 19), and 39 age and gender-matched typically developing controls. ADHD-C were characterized from ADHD-I by reduced network connectivity differences within the DMN. Additionally, reduced connectivity within the DMN was negatively associated with ADHD-RS hyperactivity-impulsivity subscale score. Compared with controls, ADHD-C but not ADHD-I differed by reduced connectivity within the DMN; inter-network connectivity between the DMN and somatomotor networks; the DMN and limbic networks; and between the somatomotor and cingulo-frontoparietal, with ventral attention and dorsal attention networks. However, graph-theoretical measures did not significantly differ between groups. These findings provide insight into the intrinsic networks underlying phenotypic differences between ADHD types. Furthermore, these intrinsic functional connectomic signatures support neurobiological differences underlying clinical variations in ADHD presentations, specifically reduced within and between functional connectivity of the DMN in the ADHD-C type.

Abstract Background The efficacy and safety of Lisdexamfetamine dimesylate (LDX) in the treatment of moderate to severe binge eating disorder (BED) has been demonstrated in multiple randomised clinical trials. Despite this, little is known about how LDX acts to improve binge eating symptoms. This study aims to provide a comprehensive understanding of the neural mechanisms by which LDX improves symptoms of BED. We hypothesise that LDX will act by normalising connectivity within neural circuits responsible for reward and impulse control, and that this normalisation will correlate with reduced binge eating episodes. Methods This is an open-label Phase 4 clinical trial of LDX in adults with moderate to severe BED. Enrolment will include 40 adults with moderate to severe BED aged 18–40 years and Body Mass Index (BMI) of 20–45 kg/m2, and 22 healthy controls matched for age, gender and BMI. Clinical interview and validated scales are used to confirm diagnosis and screen for exclusion criteria, which include comorbid anorexia nervosa or bulimia nervosa, use of psychostimulants within the past 6 months, and current use of antipsychotics or noradrenaline reuptake inhibitors. Baseline assessments include clinical symptoms, multimodal neuroimaging, cognitive assessment of reward sensitivity and behavioural inhibition, and an (optional) genetic sample. A subset of these assessments are repeated after eight weeks of treatment with LDX titrated to either 50 or 70 mg. The primary outcome measures are resting-state intrinsic connectivity and the number of binge eating episodes. Analyses will be applied to resting-state fMRI data to characterise pharmacological effects across the functional connectome, and assess correlations with symptom measure changes. Comparison of neural measures between controls and those with BED post-treatment will also be performed to determine whether LDX normalises brain function. Discussion First enrolment was in May 2018, and is ongoing. This study is the first comprehensive investigation of the neurobiological changes that occur with LDX treatment in adults with moderate to severe BED. Trial registration ACTRN12618000623291, Australian and New Zealand Clinical Trials Registry URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374913&isReview=true. Date of Registration: 20 April 2018.

Objective: Insights to underlying neural mechanisms in attention deficit hyperactivity disorder (ADHD) have emerged from neuroimaging research; however, the neural mechanisms that distinguish ADHD subtypes remain inconclusive.Method: We reviewed 19 studies integrating magnetic resonance imaging [MRI; structural (sMRI), diffusion, functional MRI (fMRI)] findings into a framework exploring pathophysiological mechanisms underlying the combined (ADHD-C) and predominantly inattentive (ADHD-I) ADHD subtypes.Results: Despite equivocal structural MRI results, findings from fMRI and DTI imaging modalities consistently implicate disrupted connectivity in regions and tracts involving frontal striatal thalamic in ADHD-C and frontoparietal neural networks in ADHD-I. Alterations of the default mode, cerebellum, and motor networks in ADHD-C and cingulo-frontoparietal attention and visual networks in ADHD-I highlight network organization differences between subtypes.Conclusion: Growing evidence from neuroimaging studies highlight neurobiological differences between ADHD clinical subtypes, particularly from a network perspective. Understanding brain network organization and connectivity may help us to better conceptualize the ADHD types and their symptom variability.

Attention Deficit Hyperactivity Disorder (ADHD) is characterized clinically by hyperactive/impulsive and/or inattentive symptoms which determine diagnostic subtypes as Predominantly Hyperactive-Impulsive (ADHD-HI), Predominantly Inattentive (ADHD-I), and Combined (ADHD-C). Neuroanatomically though we do not yet know if these clinical subtypes reflect distinct aberrations in underlying brain organization.We imaged 34 ADHD participants defined using DSM-IV criteria as ADHD-I (n=16) or as ADHD-C (n=18) and 28 matched typically developing controls, aged 8–17years, using high-resolution T1 MRI. To quantify neuroanatomical organization we used graph theoretical analysis to assess properties of structural covariance between ADHD subtypes and controls (global network measures: path length, clustering coefficient, and regional network measures: nodal degree). As a context for interpreting network organization differences, we also quantified gray matter volume using voxel-based morphometry.Each ADHD subtype was distinguished by a different organizational profile of the degree to which specific regions were anatomically connected with other regions (i.e., in “nodal degree”). For ADHD-I (compared to both ADHD-C and controls) the nodal degree was higher in the hippocampus. ADHD-I also had a higher nodal degree in the supramarginal gyrus, calcarine sulcus, and superior occipital cortex compared to ADHD-C and in the amygdala compared to controls. By contrast, the nodal degree was higher in the cerebellum for ADHD-C compared to ADHD-I and in the anterior cingulate, middle frontal gyrus and putamen compared to controls. ADHD-C also had reduced nodal degree in the rolandic operculum and middle temporal pole compared to controls. These regional profiles were observed in the context of no differences in gray matter volume or global network organization.Our results suggest that the clinical distinction between the Inattentive and Combined subtypes of ADHD may also be reflected in distinct aberrations in underlying brain organization. Keywords: ADHD, Predominantly inattentive type, Combined type, Structural connectome, Volume, Graph theory

Objective: Functional neurological symptom disorder refers to the presence of neurological symptoms not explained by neurological disease. Although this disorder is presumed to reflect abnormal function of the brain, recent studies in adults show neuroanatomical abnormalities in brain structure. These structural brain abnormalities have been presumed to reflect long-term adaptations to the disorder, and it is unknown whether child and adolescent patients, with illness that is typically of shorter duration, show similar deficits or have normal brain structure. Method: High-resolution, three-dimensional T1-weighted magnetic resonance images (MRIs) were acquired in 25 patients (aged 10–18years) and 24 healthy controls. Structure was quantified in terms of grey matter volume using voxel-based morphometry. Post hoc, we examined whether regions of structural difference related to a measure of motor readiness to emotional signals and to clinical measures of illness duration, illness severity, and anxiety/depression. Results: Patients showed greater volumes in the left supplementary motor area (SMA) and right superior temporal gyrus (STG) and dorsomedial prefrontal cortex (DMPFC) (corrected p

Background: Cognitive impairments contribute significantly to disease burden in young individuals presenting with major psychiatric disorders. The capacity to encode the consequences of one's actions may be of particular importance for real-world functioning due to its fundamental role in goal-directed behavior. Methods: Here, we investigated a dimensional measure of causal awareness during a probabilistic learning task in 92 young individuals with an admixture of major mood and psychotic illnesses, at early and more established stages. Using automated gray matter segmentation of T1-weighted images, we estimated the volume and shapes of major subcortical structures and investigated their association with causal awareness. Results: The low causal awareness (LCA) group (n = 35) reported increased social disability (p = .004) and reduced right pallidal size, specifically within the dorsolateral surfaces (p = .02), relative to the unimpaired high causal awareness (HCA) patients (n = 57). In early-stage illness, LCA had a smaller right thalamus (p = .002) relative to HCA. Exploratory investigations suggested that in developed psychotic syndromes, causal awareness was correlated with left hippocampal size (p = .006) whereas, in more persistent affective disorders, causal awareness was correlated with left amygdala size (p = .013), specifically within the anterior aspect. Discussion: Low causal awareness occurs across diagnoses and stages of illness and is associated with poor functional outcomes. Our results suggest that there may be shared neural underpinnings of its dysfunction in the early course of mood and psychotic disorders, however in more established illness, there is greater neurobiological divergence in causal awareness correlates between diagnoses.