Regional Cerebral Blood Flow Correlates of Neuropsychiatric Symptom Domains in Early Alzheimer’s Disease

Although various neuropsychiatric symptoms are frequently accompanied with Alzheimer’s disease (AD) and pose a substantial burden to both patients and caregivers, their neurobiological underpinnings remain unclear. This study investigated associations between regional cerebral blood flow (rCBF) and neuropsychiatric symptom domains in early AD. A total of 59 patients with early AD underwent brain technetium-99m hexamethylpropylene amine oxime (99mTc-HMPAO) single-photon emission computed tomography (SPECT) scans. Neuropsychiatric symptoms were assessed by the Neuropsychiatric Inventory and clustered into the affective, apathy, hyperactivity, and psychotic domains. A voxel-wise multiple regression analysis was performed with four domain scores as independent variables and age, sex, and Mini-Mental State Examination scores as covariates. The affective domain score was negatively correlated with rCBF in the prefrontal cortex, thalamus, and caudate. The apathy domain score showed inverse correlations with rCBF in the prefrontal and pre/postcentral gyri and midbrain. Patients with higher hyperactivity domain scores had increased rCBF in the prefrontal and temporal lobes. The psychotic symptom domain was positively correlated with rCBF in the cuneus and negatively associated with rCBF in the prefrontal, cingulate, and occipital regions and putamen. The score of each neuropsychiatric symptom domain showed the differential correlates of brain perfusion, while altered rCBF in the prefrontal cortex was found in all domains. Although preliminary, our results may suggest common and distinct patterns of rCBF underlying neuropsychiatric symptoms in early AD. Further studies with larger samples and control participants are warranted to confirm these findings

Diagnostic potential of multimodal neuroimaging in posttraumatic stress disorder

<div><p>Despite accumulating evidence of physiological abnormalities related to posttraumatic stress disorder (PTSD), the current diagnostic criteria for PTSD still rely on clinical interviews. In this study, we investigated the diagnostic potential of multimodal neuroimaging for identifying posttraumatic symptom trajectory after trauma exposure. Thirty trauma-exposed individuals and 29 trauma-unexposed healthy individuals were followed up over a 5-year period. Three waves of assessments using multimodal neuroimaging, including structural magnetic resonance imaging (MRI) and diffusion-weighted MRI, were performed. Based on previous findings that the structural features of the fear circuitry-related brain regions may dynamically change during recovery from the trauma, we employed a machine learning approach to determine whether local, connectivity, and network features of brain regions of the fear circuitry including the amygdala, orbitofrontal and ventromedial prefrontal cortex (OMPFC), hippocampus, insula, and thalamus could distinguish trauma-exposed individuals from trauma-unexposed individuals at each recovery stage. Significant improvement in PTSD symptoms was observed in 23%, 52%, and 88% of trauma-exposed individuals at 1.43, 2.68, and 3.91 years after the trauma, respectively. The structural features of the amygdala were found as major classifiers for discriminating trauma-exposed individuals from trauma-unexposed individuals at 1.43 years after the trauma, but these features were nearly normalized at later phases when most of the trauma-exposed individuals showed clinical improvement in PTSD symptoms. Additionally, the structural features of the OMPFC showed consistent predictive values throughout the recovery period. In conclusion, the current study provides a promising step forward in the development of a clinically applicable predictive model for diagnosing PTSD and predicting recovery from PTSD.</p></div

Classification accuracy of individual brain structural features included in the best model for distinguishing trauma-exposed individuals from trauma-unexposed individuals at time 1, 2, and 3.

<p>Classification accuracy of individual brain structural features included in the best model for distinguishing trauma-exposed individuals from trauma-unexposed individuals at time 1, 2, and 3.</p

The relationships between candidate brain structural features and the group membership at each time point.

<p>The graph presents point-biserial correlation coefficients (<i>r</i>) between candidate features and the group membership at (A) time 1, (B) time 2, and (C) time 3 assessments. Error bars represent standard errors, which were calculated using 5,000 bootstraps. Asterisks in each graph indicate the first 10 brain structural features based on the rank of the absolute <i>r</i> values. Amy, amygdala; OMPFC, orbitofrontal and ventromedial prefrontal cortex; Hippo, hippocampus; Thal, thalamus.</p

Multimodal characteristics of the amygdala, OMPFC, hippocampus, insula, and thalamus assessed at each time point.

<p>A set of candidate brain structural features was derived from multimodal neuroimaging data analysis, which comprehensively characterized local, region-wise connectivity, pair-wise connectivity, and network features of the amygdala, orbitofrontal and ventromedial prefrontal cortex (OMPFC), hippocampus, insula, and thalamus.</p

Multimodal brain structural features and their contribution to the classification of the trauma-exposed group from the trauma-unexposed group at each time point.

<p>(A) Receiver operating characteristic curves of classification models at each time point are presented. Performance of each model for classifying the trauma-exposed group from the trauma-unexposed group as a function of the subset of candidate features was measured using the AUC. The model showing the best classification performance at each time point is plotted in orange color. (B) The best subset of multimodal features for classifying the trauma-exposed group from the trauma-unexposed group at each time point is presented. Classification performance measured using the AUC for individual features is plotted in radar graphs. AUC, area under a receiver operating characteristic curve; Amy, amygdala; OMPFC, orbitofrontal and ventromedial prefrontal cortex; Hippo, hippocampus; Thal, thalamus.</p

Demographic and clinical characteristics of participants.

<p>Demographic and clinical characteristics of participants.</p

Firefighters, posttraumatic stress disorder, and barriers to treatment: Results from a nationwide total population survey

<div><p>Repeated exposure to traumatic experiences may put professional firefighters at increased risk of developing posttraumatic stress disorder (PTSD). To date, however, the rate of PTSD symptoms, unmet need for mental health treatment, and barriers to treatment have only been investigated in subsamples rather than the total population of firefighters. We conducted a nationwide, total population-based survey of all currently employed South Korean firefighters (n = 39,562). The overall response rate was 93.8% (n = 37,093), with 68.0% (n = 26,887) complete responses for all variables. The rate of current probable PTSD was estimated as 5.4%. Among those with current probable PTSD (n = 1,995), only a small proportion (9.7%) had received mental health treatment during the past month. For those who had not received treatment, perceived barriers of accessibility to treatment (29.3%) and concerns about potential stigma (33.8%) were reasons for not receiving treatment. Although those with higher PTSD symptom severity and functional impairment were more likely to seek treatment, greater symptom severity and functional impairment were most strongly associated with increased concerns about potential stigma. This nationwide study points to the need for new approaches to promote access to mental health treatment in professional firefighters.</p></div

Brain structural changes in cynomolgus monkeys administered with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine: A longitudinal voxel-based morphometry and diffusion tensor imaging study

<div><p>In animal models of Parkinson's disease (PD), 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is one of the most widely used agents that damages the nigrostriatal dopaminergic pathway. However, brain structural changes in response to MPTP remain unclear. This study aimed to investigate <i>in vivo</i> longitudinal changes in gray matter (GM) volume and white matter (WM) microstructure in primate models administered with MPTP. In six cynomolgus monkeys, high-resolution magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) scans were acquired 7 times over 32 weeks, and assessments of motor symptoms were conducted over 15 months, before and after the MPTP injection. Changes in GM volume and WM microstructure were estimated on a voxel-by-voxel basis. Mixed-effects regression models were used to examine the trajectories of these structural changes. GM volume initially increased after the MPTP injection and gradually decreased in the striatum, midbrain, and other dopaminergic areas. The cerebellar volume temporarily decreased and returned to its baseline level. The rate of midbrain volume increase was positively correlated with the increase rate of motor symptom severity (Spearman rho = 0.93, p = 0.008). Mean, axial, and radial diffusivity in the striatum and frontal areas demonstrated initial increases and subsequent decreases. The current multi-modal imaging study of MPTP-administered monkeys revealed widespread and dynamic structural changes not only in the nigrostriatal pathway but also in other cortical, subcortical, and cerebellar areas. Our findings may suggest the need to further investigate the roles of inflammatory reactions and glial activation as potential underlying mechanisms of these structural changes.</p></div

Brain structural changes in cynomolgus monkeys administered with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine: A longitudinal voxel-based morphometry and diffusion tensor imaging study - Fig 1

<p><b>Trajectories of gray matter volume changes in the first (A), second (B), and third (C) clusters over 32 weeks.</b> Solid lines represent the fitted regression lines and dotted lines show the 95% confidence intervals for the regression lines. Horizontal bars at each time point demonstrate the sample size.</p