The human connectome: A structural description of the human brain

The connection matrix of the human brain (the human "connectome") represents an indispensable foundation for basic and applied neurobiological research. However, the network of anatomical connections linking the neuronal elements of the human brain is still largely unknown. While some databases or collations of large-scale anatomical connection patterns exist for other mammalian species, there is currently no connection matrix of the human brain, nor is there a coordinated research effort to collect, archive, and disseminate this important information. We propose a research strategy to achieve this goal, and discuss its potential impact.     

Beyond the “at risk mental state” concept: transitioning to transdiagnostic psychiatry

The “at risk mental state” for psychosis approach has been a catalytic, highly productive research paradigm over the last 25 years. In this paper we review that paradigm and summarize its key lessons, which include the valence of this phenotype for future psychosis outcomes, but also for comorbid, persistent or incident non‐psychotic disorders; and the evidence that onset of psychotic disorder can at least be delayed in ultra high risk (UHR) patients, and that some full‐threshold psychotic disorder may emerge from risk states not captured by UHR criteria. The paradigm has also illuminated risk factors and mechanisms involved in psychosis onset. However, findings from this and related paradigms indicate the need to develop new identification and diagnostic strategies. These findings include the high prevalence and impact of mental disorders in young people, the limitations of current diagnostic systems and risk identification approaches, the diffuse and unstable symptom patterns in early stages, and their pluripotent, transdiagnostic trajectories. The approach we have recently adopted has been guided by the clinical staging model and adapts the original “at risk mental state” approach to encompass a broader range of inputs and output target syndromes. This approach is supported by a number of novel modelling and prediction strategies that acknowledge and reflect the dynamic nature of psychopathology, such as dynamical systems theory, network theory, and joint modelling. Importantly, a broader transdiagnostic approach and enhancing specific prediction (profiling or increasing precision) can be achieved concurrently. A holistic strategy can be developed that applies these new prediction approaches, as well as machine learning and iterative probabilistic multimodal models, to a blend of subjective psychological data, physical disturbances (e.g., EEG measures) and biomarkers (e.g., neuroinflammation, neural network abnormalities) acquired through fine‐grained sequential or longitudinal assessments. This strategy could ultimately enhance our understanding and ability to predict the onset, early course and evolution of mental ill health, further opening pathways for preventive interventions.     

Cliques and cavities in the human connectome

Encoding brain regions and their connections as a network of nodes and edges captures many of the possible paths along which information can be transmitted as humans process and perform complex behaviors. Because cognitive processes involve large, distributed networks of brain areas, principled examinations of multi-node routes within larger connection patterns can offer fundamental insights into the complexities of brain function. Here, we investigate both densely connected groups of nodes that could perform local computations as well as larger patterns of interactions that would allow for parallel processing. Finding such structures necessitates that we move from considering exclusively pairwise interactions to capturing higher order relations, concepts naturally expressed in the language of algebraic topology. These tools can be used to study mesoscale network structures that arise from the arrangement of densely connected substructures called cliques in otherwise sparsely connected brain networks. We detect cliques (all-to-all connected sets of brain regions) in the average structural connectomes of 8 healthy adults scanned in triplicate and discover the presence of more large cliques than expected in null networks constructed via wiring minimization, providing architecture through which brain network can perform rapid, local processing. We then locate topological cavities of different dimensions, around which information may flow in either diverging or converging patterns. These cavities exist consistently across subjects, differ from those observed in null model networks, and – importantly – link regions of early and late evolutionary origin in long loops, underscoring their unique role in controlling brain function. These results offer a first demonstration that techniques from algebraic topology offer a novel perspective on structural connectomics, highlighting loop-like paths as crucial features in the human brain’s structural architecture.     

Graph theoretical model of a sensorimotor connectome in zebrafish

Mapping the detailed connectivity patterns (connectomes) of neural circuits is a central goal of neuroscience. The best quantitative approach to analyzing connectome data is still unclear but graph theory has been used with success. We present a graph theoretical model of the posterior lateral line sensorimotor pathway in zebrafish. The model includes 2,616 neurons and 167,114 synaptic connections. Model neurons represent known cell types in zebrafish larvae, and connections were set stochastically following rules based on biological literature. Thus, our model is a uniquely detailed computational representation of a vertebrate connectome. The connectome has low overall connection density, with 2.45% of all possible connections, a value within the physiological range. We used graph theoretical tools to compare the zebrafish connectome graph to small-world, random and structured random graphs of the same size. For each type of graph, 100 randomly generated instantiations were considered. Degree distribution (the number of connections per neuron) varied more in the zebrafish graph than in same size graphs with less biological detail. There was high local clustering and a short average path length between nodes, implying a small-world structure similar to other neural connectomes and complex networks. The graph was found not to be scale-free, in agreement with some other neural connectomes. An experimental lesion was performed that targeted three model brain neurons, including the Mauthner neuron, known to control fast escape turns. The lesion decreased the number of short paths between sensory and motor neurons analogous to the behavioral effects of the same lesion in zebrafish. This model is expandable and can be used to organize and interpret a growing database of information on the zebrafish connectome.     

Altered expression of Alzheimer's disease-related genes in the cerebellum of autistic patients: a model for disrupted brain connectome and therapy

Autism and Alzheimer''s disease (AD) are, respectively, neurodevelopmental and degenerative diseases with an increasing epidemiological burden. The AD-associated amyloid-β precursor protein-α has been shown to be elevated in severe autism, leading to the ‘anabolic hypothesis'' of its etiology. Here we performed a focused microarray analysis of genes belonging to NOTCH and WNT signaling cascades, as well as genes related to AD and apoptosis pathways in cerebellar samples from autistic individuals, to provide further evidence for pathological relevance of these cascades for autism. By using the limma package from R and false discovery rate, we demonstrated that 31% (116 out of 374) of the genes belonging to these pathways displayed significant changes in expression (corrected P-values <0.05), with mitochondria-related genes being the most downregulated. We also found upregulation of GRIN1, the channel-forming subunit of NMDA glutamate receptors, and MAP3K1, known activator of the JNK and ERK pathways with anti-apoptotic effect. Expression of PSEN2 (presinilin 2) and APBB1 (or F65) were significantly lower when compared with control samples. Based on these results, we propose a model of NMDA glutamate receptor-mediated ERK activation of α-secretase activity and mitochondrial adaptation to apoptosis that may explain the early brain overgrowth and disruption of synaptic plasticity and connectome in autism. Finally, systems pharmacology analyses of the model that integrates all these genes together (NOWADA) highlighted magnesium (Mg²⁺) and rapamycin as most efficient drugs to target this network model in silico. Their potential therapeutic application, in the context of autism, is therefore discussed.     

The structure and measurement of human mating strategies: toward a multidimensional model of sociosexuality

Recent theoretical perspectives concerning the structure of variation in human mating have focused less on conceptualizations of alternate mating strategies and more on the evolution of a conditional strategy. Empirical evidence suggests that this conditional strategy may involve the simultaneous pursuit of long-term and short-term mating tactics. Despite these developments, empirical measurement has proceeded using the Sociosexual Orientation Inventory (SOI), which measures restricted and unrestricted mating orientations along a single bipolar continuum. To fully capture the pluralistic nature of human mating, we suggest that a multidimensional empirical measure is required. To test our hypothesis, we subjected an expanded version of the SOI, which included items measuring psychological orientation toward short-term mating and long-term mating, to principal components analysis. A three-factor structure representing short-term mating orientation, long-term mating orientation, and previous sexual behavior emerged. In subsequent analyses, we demonstrate that our newly developed long-term and short-term dimensions (a) are largely independent and (b) correlate differentially with other theoretically relevant variables.     

Characterization of a prospective human model for study of the reproductive hormone responses to major illness

With critical illness, serum testosterone levels fall markedly, whereas estrogen levels rise. Although animal studies suggest adaptive advantages, no prospective model has been available for studies in humans. We hypothesized that coronary artery bypass graft (CABG) surgery would provide such a model by eliciting the same reproductive hormone and other endocrine responses as reported with major nonsurgical illnesses. We further hypothesized that those responses would occur consistently in all CABG patients with predictable time courses, providing reliable windows for prospective studies. In 17 men undergoing CABG, serum levels of reproductive hormones, cortisol, thyroid hormones, and IGF-I were measured before and for up to 5 wk after surgery. Changes in serum levels of reproductive and other hormones were similar to those reported in nonsurgical critically ill patients. Time course for onset, duration, and recovery of reproductive hormone changes were consistent among all patients. A window for studying the testosterone and estrogen responses was established as the first 5 days following CABG. Practical use of this model was demonstrated by evaluating, in another seven men, changes in gonadotroph responsiveness to GnRH following CABG. Finally, to determine whether our findings in CABG could be extended to other surgeries, we demonstrated similar endocrine responses in 12 men following abdominal aortic aneurysm resection. We conclude that patients undergoing CABG surgery provide a useful human model for the prospective evaluation of the reproductive axis responses to acute illness. Other major surgeries are likely to also be suitable for these studies.     

An edge-centric perspective on the human connectome: link communities in the brain

Brain function depends on efficient processing and integration of information within a complex network of neural interactions, known as the connectome. An important aspect of connectome architecture is the existence of community structure, providing an anatomical basis for the occurrence of functional specialization. Typically, communities are defined as groups of densely connected network nodes, representing clusters of brain regions. Looking at the connectome from a different perspective, instead focusing on the interconnecting links or edges, we find that the white matter pathways between brain regions also exhibit community structure. Eleven link communities were identified: five spanning through the midline fissure, three through the left hemisphere and three through the right hemisphere. We show that these link communities are consistently identifiable and investigate the network characteristics of their underlying white matter pathways. Furthermore, examination of the relationship between link communities and brain regions revealed that the majority of brain regions participate in multiple link communities. In particular, the highly connected and central hub regions showed a rich level of community participation, supporting the notion that these hubs play a pivotal role as confluence zones in which neural information from different domains merges.     

Traditional treatment for mental illness in Africa: A review

The publication of The Quest for Therapy in Lower Zaire (University of California Press) by John M. Janzen (with the collaboration of William Arkinstall), and African Therapeutic Systems (Crossroads Press), edited by Z. A. Ademuwagun, John A. A. Ayoade, Ira E. Harrison and Dennis M. Warren, calls attention to recent research findings which indicate that mentally ill persons, particularly schizophrenics, may recover more rapidly and fully in non-industrialized societies than they do in industrialized ones. The books by Janzen and Ademuwagen et al. will be examined as contributions to a growing body of information on native African therapeutic practices. Evidence relating to the apparently benign course of psychosis in Africa will be examined, and various explanations for this pattern will be evaluated. Finally, some guidelines for future research will be suggested.     

Using Pareto optimality to explore the topology and dynamics of the human connectome

Graph theory has provided a key mathematical framework to analyse the architecture of human brain networks. This architecture embodies an inherently complex relationship between connection topology, the spatial arrangement of network elements, and the resulting network cost and functional performance. An exploration of these interacting factors and driving forces may reveal salient network features that are critically important for shaping and constraining the brain''s topological organization and its evolvability. Several studies have pointed to an economic balance between network cost and network efficiency with networks organized in an ‘economical’ small-world favouring high communication efficiency at a low wiring cost. In this study, we define and explore a network morphospace in order to characterize different aspects of communication efficiency in human brain networks. Using a multi-objective evolutionary approach that approximates a Pareto-optimal set within the morphospace, we investigate the capacity of anatomical brain networks to evolve towards topologies that exhibit optimal information processing features while preserving network cost. This approach allows us to investigate network topologies that emerge under specific selection pressures, thus providing some insight into the selectional forces that may have shaped the network architecture of existing human brains.     

On the epistemology of mental illness.

The most important epistemological problem in psychiatry is the detection of malingering. This is a consequence of the fact that there is no objective way to confirm any psychiatric diagnosis. Psychiatric diagnosis is based on subjective complaints. The discovery of objective markers for psychiatric diagnosis is problematic because it presupposes we can tell valid from faked subjective symptoms. But this is the difficulty. If we use pervasive irrationality as a sign of mental illness, we encounter the problem of identifying pervasive irrationality. To understand someone's behaviour, we have to assume it is largely rational. This precludes us from using behaviour to separate genuine from faked mental illness. There are a number of strategies used to solve any epistemological problem, and the most successful is the hypothetico-deductive method. If we use this, we can solve our epistemological problem. Genuine mental illness can be identified when it is the best explanation of the person's overall behaviour. Consilience of inductions is critical in supporting the validity of such explanations. This implies that it is merely a hypothesis that mental illness exists, and that we might discover that many mental illnesses, perhaps all, do not exist. We must embrace this possibility--only if we take a risk will we gain any knowledge.     

Utilization of the Behavior Change Wheel framework to develop a model to improve cardiometabolic screening for people with severe mental illness

Background

Individuals with severe mental illness (e.g., schizophrenia, bipolar disorder) die 10–25 years earlier than the general population, primarily from premature cardiovascular disease (CVD). Contributing factors are complex, but include systemic-related factors of poorly integrated primary care and mental health services. Although evidence-based models exist for integrating mental health care into primary care settings, the evidence base for integrating medical care into specialty mental health settings is limited. Such models are referred to as “reverse” integration. In this paper, we describe the application of an implementation science framework in designing a model to improve CVD outcomes for individuals with severe mental illness (SMI) who receive services in a community mental health setting.

Methods

Using principles from the theory of planned behavior, focus groups were conducted to understand stakeholder perspectives of barriers to CVD risk factor screening and treatment identify potential target behaviors. We then applied results to the overarching Behavior Change Wheel framework, a systematic and theory-driven approach that incorporates the COM-B model (capability, opportunity, motivation, and behavior), to build an intervention to improve CVD risk factor screening and treatment for people with SMI.

Results

Following a stepped approach from the Behavior Change Wheel framework, a model to deliver primary preventive care for people that use community mental health settings as their de facto health home was developed. The CRANIUM (cardiometabolic risk assessment and treatment through a novel integration model for underserved populations with mental illness) model focuses on engaging community psychiatrists to expand their scope of practice to become responsible for CVD risk, with significant clinical decision support.

Conclusion

The CRANIUM model was designed by integrating behavioral change theory and implementation theory. CRANIUM is feasible to implement, is highly acceptable to, and targets provider behavior change, and is replicable and efficient for helping to integrate primary preventive care services in community mental health settings. CRANIUM can be scaled up to increase CVD preventive care delivery and ultimately improve health outcomes among people with SMI served within a public mental health care system.

     

Temporal trends in associations between severe mental illness and risk of cardiovascular disease: A systematic review and meta-analysis

BackgroundSevere mental illness (SMI; schizophrenia, bipolar disorders (BDs), and other nonorganic psychoses) is associated with increased risk of cardiovascular disease (CVD) and CVD-related mortality. To date, no systematic review has investigated changes in population level CVD-related mortality over calendar time. It is unclear if this relationship has changed over time in higher-income countries with changing treatments.Methods and findingsTo address this gap, a systematic review was conducted, to assess the association between SMI and CVD including temporal change. Seven databases were searched (last: November 30, 2021) for cohort or case–control studies lasting ≥1 year, comparing frequency of CVD mortality or incidence in high-income countries between people with versus without SMI. No language restrictions were applied. Random effects meta-analyses were conducted to compute pooled hazard ratios (HRs) and rate ratios, pooled standardised mortality ratios (SMRs), pooled odds ratios (ORs), and pooled risk ratios (RRs) of CVD in those with versus without SMI. Temporal trends were explored by decade. Subgroup analyses by age, sex, setting, world region, and study quality (Newcastle–Ottawa scale (NOS) score) were conducted. The narrative synthesis included 108 studies, and the quantitative synthesis 59 mortality studies (with (≥1,841,356 cases and 29,321,409 controls) and 28 incidence studies (≥401,909 cases and 14,372,146 controls). The risk of CVD-related mortality for people with SMI was higher than controls across most comparisons, except for total CVD-related mortality for BD and cerebrovascular accident (CVA) for mixed SMI. Estimated risks were larger for schizophrenia than BD. Pooled results ranged from SMR = 1.55 (95% confidence interval (CI): 1.33 to 1.81, p < 0.001), for CVA in people with BD to HR/rate ratio = 2.40 (95% CI: 2.25 to 2.55, p < 0.001) for CVA in schizophrenia. For schizophrenia and BD, SMRs and pooled HRs/rate ratios for CHD and CVD mortality were larger in studies with outcomes occurring during the 1990s and 2000s than earlier decades (1980s: SMR = 1.14, 95% CI: 0.57 to 2.30, p = 0.71; 2000s: SMR = 2.59, 95% CI: 1.93 to 3.47, p < 0.001 for schizophrenia and CHD) and in studies including people with younger age. The incidence of CVA, CVD events, and heart failure in SMI was higher than controls. Estimated risks for schizophrenia ranged from HR/rate ratio 1.25 (95% CI: 1.04 to 1.51, p = 0.016) for total CVD events to rate ratio 3.82 (95% CI: 3.1 to 4.71, p < 0.001) for heart failure. Incidence of CHD was higher in BD versus controls. However, for schizophrenia, CHD was elevated in higher-quality studies only. The HR/rate ratios for CVA and CHD were larger in studies with outcomes occurring after the 1990s. Study limitations include the high risk of bias of some studies as they drew a comparison cohort from general population rates and the fact that it was difficult to exclude studies that had overlapping populations, although attempts were made to minimise this.ConclusionsIn this study, we found that SMI was associated with an approximate doubling in the rate ratio of CVD-related mortality, particularly since the 1990s, and in younger groups. SMI was also associated with increased incidence of CVA and CHD relative to control participants since the 1990s. More research is needed to clarify the association between SMI and CHD and ways to mitigate this risk.

Amanda Lambert and co-workers study associations between severe mental illness and cardiovascular disease outcomes over time.     

Characteristics of patients with mental illness and persistent high-cost status: a population-based analysis

BACKGROUND:Most of the literature on high-cost users of health care has evaluated this population as a whole, but few studies have focused on high-cost patients with mental illness and whether they persist in the high-cost state. We sought to analyze this patient population in depth and determine predictors of persistency in the high-cost state.METHODS:We used 8 years of longitudinal patient-level population data (2010–2017) from Ontario to follow high-cost patients (those in and above the 90th percentile of the cost distribution) with mental illness. We classified high-cost status, based on the proportion of the study period that patients spent in the high-cost state, as persistent (6–8 yr), sporadic (1–2 yr) or moderate (3–5 yr). We compared characteristics between groups and determined predictors of being a patient with mental illness and persistent high-cost status.RESULTS:Among 52 638 patients with mental illness and high-cost status, 18 149 (34.5%) were considered persistent high cost. These patients had higher mean annual costs of care ($44 714, 95% confidence interval [CI] $43 724–$45 703) than patients with sporadic ($23 205, 95% CI $22 741–$23 668) and moderate ($31 055, 95% CI $30 359–31 751) status, largely owing to psychiatric hospital admissions. Patients with mental illness and persistent high-cost status were more likely to be female, older, long-term residents of Ontario (information ascertained from the Immigrants, Refugees and Citizenship Canada Database), living in low-income or urban areas, or to have comorbidities. The strongest predictors of persistent (v. sporadic) high-cost status were HIV (relative risk ratio [RRR] 4.32, 95% CI 3.08–6.06), psychosis (RRR 3.41, 95% CI 3.25–3.58) and dementia (RRR 3.21, 95% CI 2.81–3.68).INTERPRETATION:Among patients with mental illness and high-cost status, persistence in the high-cost state was determined mainly by psychosis and other comorbidities. Quality-of-care interventions directed at managing psychosis and multimorbidity, as well as preventive interventions to target patients with mental illness before they enter the persistent high-cost state, are needed.

To address rising health care costs, it is important to understand the composition of the population of patients with high-cost status. Most work on high-cost patients has considered this population as a whole.^1–3 However, previous work has shown that policies and interventions designed to address quality of care and high health care spending in general will likely not apply to all subgroups of high-cost patients.^4,5 Moreover, research has shown that high-cost patients are a heterogeneous population.⁶ For example, high-cost patients who use mainly mental health services are quite different from other high-cost patients — they are younger, live in poorer neighbourhoods and have different patterns of health care use.^4,6 They also have high levels of comorbidity and incur over 30% more costs than other high-cost patients, suggesting they may be a more complex population with higher needs.^4,6It is also important to understand whether high-cost patients remain in the high-cost state for long periods of time or whether this is a one-time occurrence. The few studies that have examined persistency in the high-cost state evaluated such patients as a whole and followed them for only 3 years.^7,8 Although previous studies have found that individuals with mental illness are more likely to be patients with persistent high-cost status,^9,10 no studies have specifically examined persistency in the high-cost state among patients with mental illness⁴ or followed these patients for long periods of time.¹¹ The goal of this study was to provide an in-depth analysis of patients with mental illness and persistent high-cost status, using administrative health care data from Ontario, Canada’s most populous province.