Brain entropy and human intelligence: A resting-state fMRI study.

Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns

Collaborative treatment of traumatized children and teens : the trauma systems therapy approach/ Saxe

xiv, p. 337: ill., ind., tab.; 25 c

Collaborative treatment of traumatized children and teens : the trauma systems therapy approach/ Saxe

xiv, p. 337: ill., ind., tab.; 25 c

Collaborative treatment of traumatized children and teens : the trauma systems therapy approach/ Saxe

xiv, p. 337: ill., ind., tab.; 25 c

Machine learning methods to predict child posttraumatic stress: a proof of concept study

Abstract Background The care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine Learning (ML) computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods – as applied in other fields – produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified – from the aforementioned predictive classification models - with putative causal relations to PTSD. Methods ML predictive classification methods – with causal discovery feature selection – were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains. Results Seven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable. Conclusions In this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to replicate, refine, and extend the results produced in this study

Pathways to PTSD, Part II: Sexually Abused Children.

OBJECTIVE: The goal of this research was to develop and test a prospective model of posttraumatic stress symptoms in sexually abused children that includes pretrauma, trauma, and disclosure-related pathways. METHOD: At time 1, several measures were used to assess pretrauma variables, trauma variables, and stress reactions upon disclosure for 156 sexually abused children ages 8 to 13 years. At the time 2 follow-up (7 to 36 months following the initial interview), the children were assessed for posttraumatic stress disorder (PTSD) symptoms. RESULTS: A path analysis involving a series of hierarchically nested ordinary least squares multiple regression analyses indicated three direct paths to PTSD symptoms: avoidant coping, anxiety/arousal, and dissociation, all measured during or immediately after disclosure of sexual abuse. Additionally, age and gender predicted avoidant coping, while life stress and age at abuse onset predicted symptoms of anxiety/arousal. Taken together, these pathways accounted for approximately 57% of the variance in PTSD symptoms. CONCLUSIONS: Symptoms measured at the time of disclosure constitute direct, independent pathways by which sexually abused children are likely to develop later PTSD symptoms. These findings speak to the importance of assessing children during the disclosure of abuse in order to identify those at greatest risk for later PTSD symptoms

Brain entropy regression: WASI estimated IQ.

<p>Whole brain regression analysis was performed with AFNI to determine whether brain entropy predicts full-scale IQ as estimated from the Wechsler Abbreviated Scale of Intelligence, Matrix Reasoning subtest (uncorrected p = 0.005, cluster size = 157 voxels, corrected p = 0.001). Positive beta coefficients show where increases in resting-state brain entropy predict increases in IQ.</p

<i>β</i> coefficients, <i>p</i>-values, and <i>R</i>^<i>2</i> values from multiple regression analyses with IQ estimates as dependent variables.

<p><i>β</i> coefficients, <i>p</i>-values, and <i>R</i>^<i>2</i> values from multiple regression analyses with IQ estimates as dependent variables.</p