Modeling Intelligence As Ability To Access Multiple Brain States

MedicalResearch.com Interview with:

Glenn N. Saxe, MD Professor of Child & Adolescent Psychiatry  Hassenfeld Children’s Hospital at NYU Langone Department of Child and Adolescent Psychiatry Child Study Center, One Park Avenue New York, NY 10016

Dr. Saxe

Glenn N. Saxe, MD
Professor of Child & Adolescent Psychiatry
Hassenfeld Children’s Hospital at NYU Langone
Department of Child and Adolescent Psychiatry
Child Study Center, One Park Avenue
New York, NY 10016 

MedicalResearch.com: What is the background for this study? Would you briefly explain what is meant by brain entropy and how it relates to intelligence?

Response: Think of human intelligence as the capacity for a human being to understand their complex and ever-changing world. The world of a person is really complex and constantly in flux so the human brain must be ready to understand whatever may come – when there is no way beforehand to predict what might come. How does the brain understand its world? It creates specific models of the information it receives through specific patterns of neuronal connection. These are called brain states. The way the brain understands its world is largely through using such models, or brain states, to accurately predict what comes next. So you can see that for an intelligent brain to properly understand and predict events in the world, it will need to have access to a very, very large number of brain states. And this is how entropy is defined.

Entropy is a very old and very powerful concept in the history of science. Not only is it fundamental for thermodynamics – what we learned in high school physics – but it is also fundamental for the nature of information and it’s processing. Entropy is defined as the number of states – or distinct configurations – any system has access to at any point in time. High entropy means access to a very large number of states. Low entropy means access to a very small number of states. A solid is a phenomenon with very low entropy. A gas is a phenomenon with very high entropy. Life, and the brain, are somewhere in between.

Although it is impossible to precisely measure the number of states a brain has access to at any one moment, there is a highly related concept that can be measured. A system with access to a very high number of possible states (like a gas) has components with behavior that is highly unpredictable. A system with access to very few possible states (like a solid) has components whose behavior is highly predictable. We measured brain entropy through the predictability of the brains components at the smallest scale we had access to: what are called voxels in an fMRI scan. These are 3mm cubes of neurons in a functional MRI scan, and there are many thousands of these voxels in our measurement and each of these voxels contains information on the activity of hundreds of thousands of neurons. We measured the predictability of each of these voxels and then found clusters of voxels where their predictability – or entropy – was related to intelligence.

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Brain Imaging Associated With Heritable Cognitive Ability and Psychopathology

MedicalResearch.com Interview with:
“The Fourth Sex: Adolescent Extremes” by Victor Soto is licensed under CC BY 2.0Dag Alnaes, PhD
Norwegian Centre for Mental Disorders Research
KG Jebsen Centre for Psychosis Research
Division of Mental Health and Addiction, Oslo University Hospital
Oslo, Norway 

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: The transition from childhood to adulthood is characterized by swift and dramatic changes, both in our environment and in our brains. This period of life also coincides with the onset of many mental disorders.

To gain a better understanding of why, the clinical neurosciences must attempt to disentangle the complex and dynamic interactions between genes and the environment and how they shape our brains. The ultimate goal is to be able to predict which individuals are at risk before clinical symptoms appear. Advanced brain imaging has been proposed to represent one promising approach for such early detection, but there is currently no robust imaging marker that allows us to identify individuals at risk with any clinically relevant degree of certainty.

Our study shows that self-reported early signs of mental illness are associated with specific patterns of brain fiber pathways in young people, even if they may not fulfill criteria for a formal diagnosis or are currently in need of treatment.  Continue reading

High Rates of Amyloid Imaging Positivity in Patients With Primary Progressive Aphasia

MedicalResearch.com Interview with:

Miguel A. Santos-Santos, MD Department of Neurology, Memory and Aging Center University of California San Francisco Autonomous University of Barcelona, Cerdanyola del Valles, Spain

Dr. Miguel A. Santos-Santos

Miguel ASantosSantosMD
Department of Neurology, Memory and Aging Center
University of California San Francisco
Autonomous University of Barcelona, Cerdanyola del Valles, Spain

MedicalResearch.com: What is the background for this study?

Response: Primary progressive aphasia (PPA) is a clinically and pathologically heterogeneous (generally Frontotemporal lobar degeneration [FTLD, generally tau or tdp proteinopathies] or Alzheimer’s disease [AD] pathology) condition in which language impairment is the predominant cause of functional impairment during the initial phases of disease. Classification of PPA cases into clinical-anatomical phenotypes is of great importance because they are linked to different prevalence of underlying pathology and prediction of this pathology during life is of critical importance due to the proximity of molecule-specific therapies. The 2011 international consensus diagnostic criteria established a classification scheme for the three most common variants (the semantic [svPPA], non-fluent/agrammatic [nfvPPA], and logopenic [lvPPA]) of PPA and represent a collective effort to increase comparability between studies and improve the reliability of clinicopathologic correlations compared to the previous semantic dementia and progressive non-fluent aphasia criteria included in the 1998 consensus FTLD clinical diagnostic criteria. Since their publication, a few studies have reported amyloid imaging and pathological results in PPA, however most of these studies are retrospective in nature and the prevalence of FTLD and Alzheimer’s disease pathological findings or biomarkers in each variant has been inconsistent across the literature, therefore prospective validation with biomarker and autopsy data remains scarce and highly necessary.
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Novel Brain Imaging May Detect Preclinical Alzheimer’s Disease

MedicalResearch.com Interview with:
Dr. Sanja Josef Golubic, dr. sc

Department of Physics, Faculty of Science
University of Zagreb, Croatia

MedicalResearch.com: What is the background for this study?

Response: Our study was aimed to search the topological biomarker of Alzheimer’s disease. A recent evidences suggest that the decades long progression of brain degeneration that is irreversible by the stage of symptomatic Alzheimer’s disease, may account for failures to develop successful disease-modifying therapies. Currently, there is a pressing worldwide search for a marker of very early, possibly reversible, pathological changes related to Alzheimer’s disease in still cognitively intact individuals, that could provide a critical opportunity for evolving of efficient therapeutic interventions.

Three years ago we reported the discovery of the novel, fast brain pathway specialized for rapid processing of the simple tones. We named it gating loop. Gating loop directly links auditory brain areas to prefrontal brain area. We have also noticed the high sensitivity of the gating loop processing on AD pathology. It was inspiration to focus our Alzheimer’s disease biomarker search in the direction of prefrontal brain activation during listening of simple tones.

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Brain Imaging Patterns Moving Closer To Identifying Schizophrenia on Functional MRI

MedicalResearch.com Interview with:

Irina Rish PhD IBM T.J. Watson Research Center Yorktown Heights, NY 10598

Dr. Rish

Irina Rish PhD
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598 

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: Schizophrenia is a chronic and severe psychiatric disorder that affects roughly about 1% of population. Although it is not as common as other mental disorders, such as depression, anxiety, and attention deficit disorder (ADD), and so on, schizophrenia  is perhaps one of  the most debilitating psychiatric disorders,  preventing people from normal  functioning in daily life. It is characterized primarily by a range of psychotic symptoms, including hallucinations (false auditory, visual or tactile perceptions detached from reality), as well as delusions, disorganized thoughts, speech and behavior, and multiple other symptoms including difficulty showing (and recognizing) emotions, poor executive functioning, inattentiveness, problems with working memory,  and so one. Overall, schizophrenia has a devastating impact not only on patients and their families, but on the economy, as it was estimated to cost the US about 2% off  gross national product in treatment costs, missed work, etc.
Thus, taking steps towards better understanding of the disease can potentially lead to more accurate early diagnosis and better treatments.

In this work, the objective was to identify “statistical biomarkers’ of schizophrenia from brain imaging data (specifically, functional MRI), i.e. brain activity patterns that would be capable of accurately discriminating between schizophrenic patients and controls, and reproducible (stable) across multiple datasets. The focus on both predictive accuracy (generalization to previously unseen subjects) as well as on stability (reproducibility) across multiple datsets differentiates our work from majority of similar studies in neuroimaging field that tend to focus only on statistically significant differences between such patterns on a fixed dataset, and may not reliably generalize to new data.

Our prior work on neuroimaging-based analysis of schizoprenia http://journals.plos.org/plosone/article/related?id=10.1371/journal.pone.0050625, as well as other research in the field, suggest that disrupted functional connectivity can be a much more informative source of discriminative patterns than local changes in brain activations, since schizophrenia is well known to be a “network disease”, rather than a localized one.

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Cerebral Perfusion Is Perturbed by Preterm Birth and Brain Injury

MedicalResearch.com Interview with:
Eman S. Mahdi, MD, MBChB
Pediatric Radiology Fellow

Catherine Limperopoulos, PhD Director, Developing Brain Research Laboratory Co-Director of Research, Division of Neonatology Diagnostic Imaging and Radiology Children’s National Health System Washington, DC

Dr. Catherine Limperopoulos

Catherine Limperopoulos, PhD
Director, Developing Brain Research Laboratory
Co-Director of Research, Division of Neonatology
Diagnostic Imaging and Radiology
Children’s National Health System
Washington, DC

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: Premature birth is a major public health concern in the United States affecting 1 in 10 infants each year. Prematurity-related brain injury is very common and associated with a high prevalence of brain injury and accompanying lifelong neurodevelopmental morbidities.

Early disturbances in systemic and cerebral hemodynamics are thought to mediate prematurity-related brain injury. The extent to which cerebral blood flow (CBF) is disturbed in preterm birth is poorly understood, in large part because of the lack of monitoring techniques that can directly and non-invasively measure cerebral blood flow.

We report for the first time early disturbances in global and regional cerebral blood flow in preterm infants following brain injury on conventional magnetic resonance imaging (MRI) over the third trimester of ex-uterine life using arterial spin labelling images. In terms of regional differences, we saw a marked decrease in blood flow to the thalamus and the pons, regions known to be metabolically active during this time.

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Neuroimaging Detects Chemical Disturbances in Stuttering

MedicalResearch.com Interview with:
Joseph O’Neill, PhD
Division of Child and Adolescent Psychiatry
University of California–Los Angeles Semel Institute for Neuroscience
Los Angeles

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: Stuttering seriously diminishes quality of life. While many children who stutter eventually grow out of it, stuttering does persist into adulthood in many others, despite treatment. Like earlier investigators, we are using neuroimaging to explore possible brain bases of stuttering, aiming, eventually, to improve prognosis. What’s novel is that our study deploy neuroimaging modalities– arterial spin labelling and, in this paper, magnetic resonance spectroscopy (MRS)– not previously employed in stuttering. MRS offers prospects of detecting possible neurochemical disturbances in stuttering.

The MRS results showed differences in neurometabolite– brain chemicals– levels between people who stutter (adults and children) and those who don’t in many brain regions where other neuroimaging has also observed effects of stuttering. In particular, MRS effects were apparent in brain circuits where our recent fMRI work detected signs of stuttering, circuits subserving self-regulation of speech production, attention and emotion. This reinforces the idea that stuttering has to do with how the brain manages its own activity along multiple dimensions: motivation, allocation of resources, and behavioral output.

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