Imaging in Psychiatric Disorders

August 25 - September 2

Coordinator: Sidney H. Kennedy

University of Toronto (Canada)

Faculties:

Rita Goldstein, Brookhaven National Laboratory, Upton, USA

Tomas Hajek, Dalhousie University, Halifax, Canada

Andreas Meyer-Lindenberg, Central Institute Mental Health, Mannheim, Germany

Jonathan Roiser, University College, London, UK

Antonio Strafella, University of Toronto, Canada

Michael Weiner, University of California, San Francisco, USA

The introduction of neuroimaging to the fields of psychiatry and neuroscience has revolutionized the way we are able to study the underlying biological mechanisms of Mental, Neurological and Substance use (MNS) disorders. Since the recognition of enlarged ventricle size in schizophrenia with computed tomography more than three decades ago, higher resolution magnetic resonance imaging (MRI) techniques, including diffusion tensor imaging (DTI) have helped to identify variants in neuroanatomy and neural connectivity across neuropsychiatric disorders. Functional imaging through positron emission tomography (PET), single photon emission computed tomography (SPECT) and functional MRI (fMRI) display brain activity in the resting state and in response to paradigms designed to evaluate various aspects of a disorder including motivation, cognition, affect, executive function, and social communication. In this course, international experts will begin by describing the historical relevance of neuroimaging as well as critically appraise its contribution within the context of their individual areas of research.

From maps to mechanisms through imaging of Schizophrenia

Andreas Meyer-Lindenberg

Functional and structural brain imaging has identified neural and neurotransmitters systems involved in schizophrenia, and their link to cognitive and behavioural disturbances such as psychosis. However, mapping such abnormalities in patients cannot fully capture the strong neurodevelopmental component of schizophrenia, which predates the manifestation of this illness. A recent strategy has been to focus on mechanisms of disease risk. Through imaging genetics, neural systems mediating heritable risk linked to candidate and genome-wide supported common variants have been defined, and mechanisms for environmental risk and gene-environment interactions are emerging. Characterizing the neural risk architecture of schizophrenia provides a translational research strategy for future treatments.

Mental health and social life are intimately interrelated, as demonstrated by the frequent social deficits of psychiatric patients and the increased rate of psychiatric disorders in individuals exposed to social environmental adversity. Emerging evidence that combines epidemiology, social psychology and neuroscience to bring neural mechanisms of social risk factors for mental illness into focus will be reviewed. In doing so, prior evidence on the effects of common genetic risk factors in social neural pathways and outline the need for integrative approaches to identify the converging mechanisms of social environmental and genetic risk in brain will be discussed.

A cognitive neuropsychological model of depression

Jonathan Roiser

Studies in the 1990s used fMRI and PET to examine brain abnormalities and resting state metabolism in depression. During the same time period, a number of anatomical studies in non-human primates reported evidence that depression is caused by alterations in a "visceromotor" network including specific parts of the prefrontal cortex, the subgenual portion of the anterior cingulated cortex and their subcortical projection targets. Subsequent work using fMRI suggests that the visceromotor network participates in the brain's processing of and response to emotional and rewarding stimuli (known as hot cognition) and that this network is functionally disruptive in patients with Major Depressive Disorders (MDD). Focus will be on behavioural studies which provide strong evidence for abnormal "hot" and "cold" cognition in depression as well as an important complementary hypothesis to the visceromotor network known as the cognitive neuropsychological model of depression. This model stresses the critical role of negative emotional and information processing biases in the etiology and recovery from depression. New research which extends this model will also be critically analyzed.

From neuroimaging to neurostimulation

Sidney Kennedy

The emergence of neurostimulation therapies, including Deep Brain Stimulation (DBS) for treatment resistant depression (TRD) is a promising development that involves the bilateral implantation of electrodes to a neuroanatomical site, which receives remote electrical stimulation via a subclavicularly implanted pacemaker. Through the use of neuroimaging, and other biomarker testing, there is an opportunity to elucidate both the underlying circuitry of TRD and the mechanism of action of DBS. Studies on the neurocircuitry of depression support hyperactivity in the subcallosal cingulate gyrus-Area 25 (SCg25), which is ameliorated by neuromodulation treatments. The Neurostimulation team at University Health Network, University of Toronto has a seven-year experience involving over 30 patients who have received SCg25 DBS, and have been followed up to 6 years. These data support the long-term effectiveness of DBS for TRD, while highlighting the mortality rate associated with treatment resistance. Beyond the initial DBS trial to the SCg25, additional neuroanatomical targets are being explored, including the nucleus accumbens and internal capsule/ventral striatum, lateral habenula, and inferior thalamic peduncle. To date, there are open-label reports on over 100 subjects, demonstrating acute and sustained effectiveness and safety. However, until published randomized controlled trials establish efficacy for this invasive treatment, the medical community and the media need to exercise caution in their enthusiastic endorsement of this appealing advance in psychiatry. The potential for neuroimaging advances to identify discrete targets for further neuromodulation treatments will be a topic for broader discussion.

Differential diagnosis of brain structural changes in bipolar disorders – Nietzsche, Cade, neurogenesis and deep brain stimulation

Tomas Hajek

Can we use brain structural changes to identify subjects at risk for mood disorders? Are mood disorders neurodegenerative? Does adversity change the structure of our brain? Has neuroimaging advanced development of new treatments? Can we alter the brain hardware by medications? Neuroimaging techniques are helping us answer these questions.

Whereas overall there is a strong evidence for the presence of structural and functional variations in the brains of bipolar patients, the interpretation of these findings is difficult. Clinical factors, such as stage and burden of illness, family history, presence of comorbid conditions, as well as exposure to medication may all impact neuroimaging findings. Neuroanatomical abnormalities reported in bipolar patients may either represent inherited risk factors for bipolar disorders (BD) or emerge as secondary to the burden of illness, co-morbid conditions or medication exposure. Distinguishing between the factors predisposing for or resulting from the illness is needed to improve our understanding of BD. This is also critical for clinical reasons. Whereas the biological risk factors may aid in early diagnosis, the changes secondary to the burden of illness may be a useful outcome measure for interventions.

This part of the course will focus on isolating the individual factors, which affect the brain structure and function in BD. We will discuss 1) brain changes as markers of vulnerability for mood disorders, with the goal of identifying subjects at increased risk for the illness; 2) mood disorders as mild neurodegenerative illnesses, with the goal of identifying novel treatment targets; 3) neuroprotective effects of lithium, with the goal of preventing or correcting brain structural changes in BD or neurodegenerative disorders; 4) the effects of medical conditions comorbid with BD (diabetes mellitus, migraine) on the brain, with the goal of gaining novel insights into pathophysiology and treatment of mood disorders; 5) circuits involved in mood regulation, with the goal of identifying targets for neurosurgical treatments of mood disorders.

At the end of this session, the participants will have an understanding of 1) the main neuroimaging findings in BD; 2) the factors affecting brain structure and function in mood disorders; 3) the main limitations of neuroimaging studies; 4) the practical implications of neuroimaging findings for patients with mood disorders.

Generalized Anxiety Disorder. Anxiety and Neuroimaging: A focus on the neurocircuitry of fear

Dan Stein

Some of the putative cognitive-affective disturbances in the anxiety disorders, their underlying neural circuitry, and the relevant molecular systems will be the key topics of this section of the Course. A broad range of imaging modalities and paradigms has helped shed light on the psychobiology of the anxiety disorders. Beyond the amygdala, the insula and anterior cingulate cortex have also been implicated in the neurocircuitry of fear. There are consistent findings among the anxiety disorders that this network displays hyperactivity in response to symptom provocation. Attention will be given both to concepts and data involving proximal mechanisms (e.g. gene-environment interactions), as well as distal mechanisms (i.e. evolved mechanisms). Taken together this work has led to a model which emphasizes the role of frontal-limbic circuitry as well as of monoamine and amino acid neurotransmitters in underpinning heightened responses to threat in individuals with anxiety disorders. Neuroimaging studies have also contributed to understanding pharmacological and psychotherapy effects on various anxiety disorders and have the potential to guide future therapeutic strategies.

Contributions of the Alzheimer's Disease Neuroimaging Initiative

Michael Weiner

During the past decade there has been an explosion in research using brain MRI PET and SPECT scanning to investigate changes in the brain which occur during normal aging and neurodegenerative diseases including: Alzheimer's disease, cerebrovascular disease, frontotemporal dementias, Parkinson's disease, Lewy Body disease, and epilepsy, as well as other conditions which appear to be associated with neurodegeneration including depression, post traumatic stress disorder, and a wide variety of other conditions. A very wide variety of MRI contrasts are used including structural imaging with T1, T2 and T2* weighting, diffusion weighted and diffusion tensor imaging, arterial spin label perfusion imaging, fMRI include task activated and resting state studies. PET and SPECT imaging uses a variety of ligands including radioactive glucose, and markers of dopamine transporters and brain amyloid. We will start with a critical reappraisal of this entire field and we will incorporate unanswered questions and challenges for the future.

Until recently, Alzheimer's disease (AD) has been defined clinically, and imaging with MRI, PET and fluid biomarkers has solely been used for research. The original ADNI (from 2004-2010) was a $67 million observational longitudinal clinical trial with the overall goal of determining the value of MRI and PET imaging together with blood and CSF biomarkers for disease modifying Alzheimer's treatment trials. Specific aims were to: improve methods for clinical trials; determine the optimum methods for acquiring and processing images and biomarkers; "Validate" imaging and biomarker data by correlating with neuropsychological and behavioral data. We longitudinally studied: MCI (n= 400); AD (n= 200); Controls (n= 220). Clinical visits include neuropsychological assessments, MMSE, CDR, ADAS-cog, MRI (1.5 T), FDG PET blood and urine and CSF. The results showed that the rate of hippocampus atrophy has high statistical power for measuring change over time, and further that changes in this same region predict conversion from MCI to AD. FDG PET measures also have high predictive value of MCI conversion to AD and cognitive decline, and some FDG PET measures have high power as outcomes. Furthermore, analysis of data from the normal controls suggests that normal healthy elders with APOE4 and/or low CSF Aβ amyloid have worse memory scores and higher rates of hippocampal atrophy than the non carrier controls or subjects with high CSF Aβ; this is consistent with the hypothesis that some controls have preclinical AD pathology. The recent availability of F18 amyloid imaging has been exensively used and more than 400 F18 florbetapir scans have been performed. The results are already showing that normal and MCI subjects who are amyloid positive show greater rates of decline than amyloid negative subjects. Similar ADNI-like projects, with similar methods, are underway in Australia, Japan, Europe, China, Taiwan, and Korea leading to the "World Wide ADNI network." Following the original ADNI we were renewed for another large 5 years study called ADNI 2 (total funding for all ADNI projects now $140 million) which performs 3Tesla MRI and F18 amyloid PET imaging with Florbetapir on an additional 150 controls, 300 subjects with early MCI, 150 subjects with late MCI and 150 subjects with dementia due to AD. ADNI methods are now widely used in clinical treatment trials, and have led to the development of the "new" research criteria for AD developed by the Alzheimer's Association and NIH. Updated analysis of ADNI follow-up data will be presented.

Neurogimaging, dopamingeric transmission and cognitive-behavioural complications in Parkinson's disease

Antonio Strafella

A key indicator of Parkinson's Disease (PD) is the loss of midbrain dopaminergic (mDA) projections to the striatum. Consequently, patients suffer from increasingly impaired motor control. To combat this symptom, dopamine replacement treatment is used to manage deficiencies in the motor basal ganglia network. Yet, this may result in a dopaminergic 'over-dosing effect' as a result of which non-motor symptoms are worsened and certain forms of behavioural addiction are activated. An important element of research in PD is the implication of dopamine agonists in the development of Impulse Control Disorders (ICDs such as hypersexuality, compulsive shopping, compulsive eating and pathological gambling. Recent functional imaging studies conducted in PD patients with and without ICDs provide evidence of significant abnormalities in prefrontal-striatal areas associated with decision making and reward. Proposed mechanisms include abnormal functioning of mesolimbic structures resulting in dysregulation of dopamine.

Integrating neuroimaging, cognitive neuroscience and neuropsychology in understanding and treating addictions

Rita Goldstein

The traditional concept of the role of reward circuitry as mediated by mesolimbic structures is now challenged by recent human neuroimaging studies which have implicated other areas of the brain such as the prefrontal cortex as having an essential role in addiction. Techniques such as fMRI, PET and event related potentials are used to explore the neurobiology underlying the core psychological impairments in drug addiction (impulsivity, motivation, drive/motivation, insight/awareness) as associated with its clinical symptomatology (intoxication, craving, binging and withdrawal). The theoretical model that guides this research is known as iRISA (Impaired Response Inhibition and Salience Attribution), which states that the motivation to procure drugs overpowers the drive to attain non-drug related goals. Topics to be covered include the role of abstinence in enhancing recovery in treatment seeking cocaine addicted individuals as well results from novel pharmacological fMRI studies which may aid in the development of neurorehabilitation strategies in those with cocaine addiction.

In addition to the topics listed, the Course will also train participants on the prediction of emerging disorders in at-risk/prodromal subpopulations; the identification of neuroimaging biomarkers that moderate and mediate treatment outcomes and the potential to identify novel therapeutic targets. The potential for integration of imaging and non-imaging predictors of treatment outcome using complex bioinformatics will also be considered as a future direction.