During rest, brain activity is synchronized between different regions widely distributed throughout the brain, forming functional networks. However, the molecular mechanisms supporting functional connectivity remain undefined. We show that functional brain networks defined with resting-state functional magnetic resonance imaging can be recapitulated by using measures of correlated gene expression in a post mortem brain tissue data set. The set of 136 genes we identify is significantly enriched for ion channels. Polymorphisms in this set of genes significantly affect resting-state functional connectivity in a large sample of healthy adolescents. Expression levels of these genes are also significantly associated with axonal connectivity in the mouse. The results provide convergent, multimodal evidence that resting-state functional networks correlate with the orchestrated activity of dozens of genes linked to ion channel activity and synaptic function.

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With the advent of new analysis methods in neuroimaging that involve independent component analysis (ICA) and dynamic causal modelling (DCM), investigations have focused on measuring both the activity and connectivity of specific brain networks. In this study we combined DCM with spatial ICA to investigate network switching in the brain. Using time courses determined by ICA in our dynamic causal models, we focused on the dynamics of switching between the default mode network (DMN), the network which is active when the brain is not engaging in a specific task, and the central executive network (CEN), which is active when the brain is engaging in a task requiring attention. Previous work using Granger causality methods has shown that regions of the brain which respond to the degree of subjective salience of a stimulus, the salience network, are responsible for switching between the DMN and the CEN (Sridharan et al., 2008). In this work we apply DCM to ICA time courses representing these networks in resting state data. In order to test the repeatability of our work we applied this to two independent datasets. This work confirms that the salience network drives the switching between default mode and central executive networks and that our novel technique is repeatable.

A comprehensive account of the causes of alcohol misuse must accommodate individual differences in biology, psychology and environment, and must disentangle cause and effect. Animal models1 can demonstrate the effects of neurotoxic substances; however, they provide limited insight into the psycho-social and higher cognitive factors involved in the initiation of substance use and progression to misuse. One can search for pre-existing risk factors by testing for endophenotypic biomarkers2 in non-using relatives; however, these relatives may have personality or neural resilience factors that protect them from developing dependence3. A longitudinal study has potential to identify predictors of adolescent substance misuse, particularly if it can incorporate a wide range of potential causal factors, both proximal and distal, and their influence on numerous social, psychological and biological mechanisms4. Here we apply machine learning to a wide range of data from a large sample of adolescents (n = 692) to generate models of current and future adolescent alcohol misuse that incorporate brain structure and function, individual personality and cognitive differences, environmental factors (including gestational cigarette and alcohol exposure), life experiences, and candidate genes. These models were accurate and generalized to novel data, and point to life experiences, neurobiological differences and personality as important antecedents of binge drinking. By identifying the vulnerability factors underlying individual differences in alcohol misuse, these models shed light on the aetiology of alcohol misuse and suggest targets for prevention.

Advances in therapeutic strategies for Alzheimer's disease that lead to even small delays in onset and progression of the condition would significantly reduce the global burden of the disease. To effectively test compounds for Alzheimer's disease and bring therapy to individuals as early as possible there is an urgent need for collaboration between academic institutions, industry and regulatory organizations for the establishment of standards and networks for the identification and qualification of biological marker candidates. Biomarkers are needed to monitor drug safety, to identify individuals who are most likely to respond to specific treatments, to stratify presymptomatic patients and to quantify the benefits of treatments. Biomarkers that achieve these characteristics should enable objective business decisions in portfolio management and facilitate regulatory approval of new therapies.

SummaryBackgroundAmyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease of upper and lower motor neurons, associated with frontotemporal dementia (FTD) in about 14% of incident cases. We assessed the frequency of the recently identified C9orf72 repeat expansion in familial and apparently sporadic cases of ALS and characterised the cognitive and clinical phenotype of patients with this expansion.MethodsA population-based register of patients with ALS has been in operation in Ireland since 1995, and an associated DNA bank has been in place since 1999. 435 representative DNA samples from the bank were screened using repeat-primed PCR for the presence of a GGGGCC repeat expansion in C9orf72. We assessed clinical, cognitive, behavioural, MRI, and survival data from 191 (44%) of these patients, who comprised a population-based incident group and had previously participated in a longitudinal study of cognitive and behavioural changes in ALS.FindingsSamples from the DNA bank included 49 cases of known familial ALS and 386 apparently sporadic cases. Of these samples, 20 (41%) cases of familial ALS and 19 (5%) cases of apparently sporadic ALS had the C9orf72 repeat expansion. Of the 191 patients for whom phenotype data were available, 21 (11%) had the repeat expansion. Age at disease onset was lower in patients with the repeat expansion (mean 56·3 [SD 8·3] years) than in those without (61·3 [10·6] years; p=0·043). A family history of ALS or FTD was present in 18 (86%) of those with the repeat expansion. Patients with the repeat expansion had significantly more co-morbid FTD than patients without the repeat (50% vs 12%), and a distinct pattern of non-motor cortex changes on high-resolution 3 T magnetic resonance structural neuroimaging. Age-matched univariate analysis showed shorter survival (20 months vs 26 months) in patients with the repeat expansion. Multivariable analysis showed an increased hazard rate of 1·9 (95% 1·1–3·7; p=0·035) in those patients with the repeat expansion compared with patients without the expansionInterpretationPatients with ALS and the C9orf72 repeat expansion seem to present a recognisable phenotype characterised by earlier disease onset, the presence of cognitive and behavioural impairment, specific neuroimaging changes, a family history of neurodegeneration with autosomal dominant inheritance, and reduced survival. Recognition of patients with ALS who carry an expanded repeat is likely to be important in the context of appropriate disease management, stratification in clinical trials, and in recognition of other related phenotypes in family members.FundingHealth Seventh Framework Programme, Health Research Board, Research Motor Neuron, Irish Motor Neuron Disease Association, The Motor Neurone Disease Association of Great Britain and Northern Ireland, ALS Association.

A number of neuroimaging candidate markers are promising, such as hippocampus and entorhinal cortex volumes, basal forebrain nuclei, cortical thickness, deformation-based and voxel-based morphometry, structural and effective connectivity by using diffusion tensor imaging, tractography, and functional magnetic resonance imaging. CSF Abeta42, BACE1, total tau, and p-tau are substantially altered in MCI and clinical AD. Other interesting novel marker candidates derived from blood are being currently proposed (phase I). Biomarker discovery through proteomic approaches requires further research. Large-scale international controlled multicenter trials (such as the U.S., European, Australian, and Japanese Alzheimer's Disease Neuroimaging Initiative and the German Dementia Network) are engaged in phase III development of the core feasible imaging and CSF biomarker candidates in AD. Biomarkers are in the process of implementation as primary outcome variables into regulatory guideline documents regarding study design and approval for compounds claiming disease modification.

The traditional view that mental disorders are distinct, categorical disorders has been challenged by evidence that disorders are highly comorbid and exist on a continuum (e.g., Caspi et al., 2014; Tackett et al., 2013). The first objective of this study was to use structural equation modeling to model the structure of psychopathology in an adolescent community-based sample (N = 2,144) including conduct disorder, attention-deficit/hyperactivity disorder (ADHD), oppositional-defiant disorder (ODD), obsessive–compulsive disorder, eating disorders, substance use, anxiety, depression, phobias, and other emotional symptoms, assessed at 16 years. The second objective was to identify common personality and cognitive correlates of psychopathology, assessed at 14 years. Results showed that psychopathology at 16 years fit 2 bifactor models equally well: (a) a bifactor model, reflecting a general psychopathology factor, as well as specific externalizing (representing mainly substance misuse and low ADHD) and internalizing factors; and (b) a bifactor model with a general psychopathology factor and 3 specific externalizing (representing mainly ADHD and ODD), substance use and internalizing factors. The general psychopathology factor was related to high disinhibition/impulsivity, low agreeableness, high neuroticism and hopelessness, high delay-discounting, poor response inhibition and low performance IQ. Substance use was specifically related to high novelty-seeking, sensation-seeking, extraversion, high verbal IQ, and risk-taking. Internalizing psychopathology was specifically related to high neuroticism, hopelessness and anxiety-sensitivity, low novelty-seeking and extraversion, and an attentional bias toward negatively valenced verbal stimuli. Findings reveal several nonspecific or transdiagnostic personality and cognitive factors that may be targeted in new interventions to potentially prevent the development of multiple psychopathologies.