Patterns of brain structural connectivity differentiate normal weight from overweight subjects

AbstractBackgroundAlterations in the hedonic component of ingestive behaviors have been implicated as a possible risk factor in the pathophysiology of overweight and obese individuals. Neuroimaging evidence from individuals with increasing body mass index suggests structural, functional, and neurochemical alterations in the extended reward network and associated networks.AimTo apply a multivariate pattern analysis to distinguish normal weight and overweight subjects based on gray and white-matter measurements.MethodsStructural images (N = 120, overweight N = 63) and diffusion tensor images (DTI) (N = 60, overweight N = 30) were obtained from healthy control subjects. For the total sample the mean age for the overweight group (females = 32, males = 31) was 28.77 years (SD = 9.76) and for the normal weight group (females = 32, males = 25) was 27.13 years (SD = 9.62). Regional segmentation and parcellation of the brain images was performed using Freesurfer. Deterministic tractography was performed to measure the normalized fiber density between regions. A multivariate pattern analysis approach was used to examine whether brain measures can distinguish overweight from normal weight individuals.Results1. White-matter classification: The classification algorithm, based on 2 signatures with 17 regional connections, achieved 97% accuracy in discriminating overweight individuals from normal weight individuals. For both brain signatures, greater connectivity as indexed by increased fiber density was observed in overweight compared to normal weight between the reward network regions and regions of the executive control, emotional arousal, and somatosensory networks. In contrast, the opposite pattern (decreased fiber density) was found between ventromedial prefrontal cortex and the anterior insula, and between thalamus and executive control network regions. 2. Gray-matter classification: The classification algorithm, based on 2 signatures with 42 morphological features, achieved 69% accuracy in discriminating overweight from normal weight. In both brain signatures regions of the reward, salience, executive control and emotional arousal networks were associated with lower morphological values in overweight individuals compared to normal weight individuals, while the opposite pattern was seen for regions of the somatosensory network.Conclusions1. An increased BMI (i.e., overweight subjects) is associated with distinct changes in gray-matter and fiber density of the brain. 2. Classification algorithms based on white-matter connectivity involving regions of the reward and associated networks can identify specific targets for mechanistic studies and future drug development aimed at abnormal ingestive behavior and in overweight/obesity

Electrogastrography: A Noninvasive Technique to Evaluate Gastric Electrical Activity

Electrogastrography (EGG) is the recording of gastric electrical activity (GEA) from the body surface. The cutaneous signal is low in amplitude and consequently must be amplified considerably. The resultant signal is heavily contaminated with noise, and visual analysis alone of an EGG signal is inadequate. Consequently, EGG recordings require special methodology for acquisition, processing and analysis. Essential components of this methodology involve an adequate system of digital filtering, amplification and analysis, along with minimization of the sources of external noise (random motions of the patient, electrode-skin interface impedance, electrode bending, obesity, etc) and a quantitative interpretation of the recordings. There is a close relationship between GEA and gastric motility. Although it has been demonstrated that EGG satisfactorily reflects internal GEA frequency, there is not acceptable correlation with gastric contractions or gastric emptying. Many attempts have been made to relate EGG 'abnormalities' with clinical syndromes and diseases; however, the diagnostic and clinical value of EGG is still very much in question

Association between gastric electromechanical activity and satiation in dogs

Objective: The objective of this study was to validate the use of impedance for measurement of antral contractions and to determine the relationship between food-induced changes in gastric motility and satiation. Research Methods and Procedures: In Experiment 1, three dogs were implanted with an antral strain gauge and bipolar electrodes for measurement of local tissue impedance. Impedance and strain gauge recordings were obtained simultaneously during antral contractions to correlate impedance changes with contractile events. In Experiment 2, seven dogs were implanted with two pairs of gastric electrodes for simultaneous recording of slow wave activity and impedance. The changes in the rate of slow waves and of antral contractions assessed by impedance during food intake were characterized. Results: Variations in strain gauge amplitude were highly correlated with changes in antral impedance (R2: 0.70 to 0.82, p \u3c 0.05). In Experiment 2, slow wave rate was significantly reduced after food intake and reached a nadir at satiation (5.0 ± 0.3 vs. 3.8 ± 0.5 events/min, p \u3c 0.001). Likewise, the amplitude of antral contractions assessed by variations in impedance was significantly increased after food intake, peaking at satiation (5.3 ± 1.4 vs. 12.2 ± 4.3 Ohms, p \u3c 0.01). Discussion: Measurement of impedance is a reliable tool for assessing gastric contractility. Food ingestion significantly reduces slow wave rate and enhances antral contractions. Peak changes in these two variables occur at the time of satiation. Electrical measurements of both slow waves and impedance may be used to estimate gastric motility and satiation. Copyright © 2007 NAASO