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Introduction The semantic network is an important mediator of language, enabling both speech production and the comprehension of multimodal stimuli. A major challenge in the field of neurosurgery is preventing semantic deficits. Multiple cortical areas have been linked to semantic processing, though knowledge of network connectivity has lacked anatomic specificity. Using attentional task‐based fMRI studies, we built a neuroanatomical model of this network. Methods One hundred and fifty‐five task‐based fMRI studies related to categorization of visual words and objects, and auditory words and stories were used to generate an activation likelihood estimation (ALE). Cortical parcellations overlapping the ALE were used to construct a preliminary model of the semantic network based on the cortical parcellation scheme previously published under the Human Connectome Project. Deterministic fiber tractography was performed on 25 randomly chosen subjects from the Human Connectome Project, to determine the connectivity of the cortical parcellations comprising the network. Results The ALE analysis demonstrated fourteen left hemisphere cortical regions to be a part of the semantic network: 44, 45, 55b, IFJa, 8C, p32pr, SFL, SCEF, 8BM, STSdp, STSvp, TE1p, PHT, and PBelt. These regions showed consistent interconnections between parcellations. Notably, the anterior temporal pole, a region often implicated in semantic function, was absent from our model. Conclusions We describe a preliminary cortical model for the underlying structural connectivity of the semantic network. Future studies will further characterize the neurotractographic details of the semantic network in the context of medical application.

Background The default mode network (DMN) is an important mediator of passive states of mind. Multiple cortical areas, such as the anterior cingulate cortex, posterior cingulate cortex, and lateral parietal lobe, have been linked in this processing, though knowledge of network connectivity had limited tractographic specificity. Methods Using resting‐state fMRI studies related to the DMN, we generated an activation likelihood estimation (ALE). We built a tractographical model of this network based on the cortical parcellation scheme previously published under the Human Connectome Project. DSI‐based fiber tractography was performed to determine the structural connections between cortical parcellations comprising the network. Results Seventeen cortical regions were found to be part of the DMN: 10r, 31a, 31pd, 31pv, a24, d23ab, IP1, p32, POS1, POS2, RSC, PFm, PGi, PGs, s32, TPOJ3, and v23ab. These regions showed consistent interconnections between adjacent parcellations, and the cingulum was found to connect the anterior and posterior cingulate clusters within the network. Conclusions We present a preliminary anatomic model of the default mode network. Further studies may refine this model with the ultimate goal of clinical application.

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mHealth (mobile health) refers to mobile technologies that aid medical and public health practices. As of February 2019, 81% of Americans own a smartphone, and mHealth applications (apps) have become increasingly common with more than 400,000 mHealth applications currently available. Advancements in mobile technology now allow us to provide personalized up-to-date information, track personal health data, remind and engage patients, and communicate in a cost-effective way. There are new opportunities for healthcare providers to integrate mHealth into clinical practice. We discuss the current scientific evidence, and research into mHealth technology.

The kynurenine pathway (KP) is a major route for L-Tryptophan (L-TRP) metabolism, yielding a variety of bioactive compounds including kynurenic acid (KYNA), 3-hydroxykynurenine (3-HK), quinolinic acid (QUIN), and picolinic acid (PIC). These tryptophan catabolites are involved in the pathogenesis of many neuropsychiatric disorders, particularly when the KP becomes dysregulated. Accordingly, the enzymes that regulate the KP such as indoleamine/tryptophan 2, 3-dioxygenase (IDO-1/TDO), kynurenine aminotransferases (KATs), and kynurenine 3-monooxygenase (KMO) represent potential drug targets, as enzymatic inhibition can favorably rebalance KP metabolite concentrations. In addition, the galantamine-memantine combination, through its modulatory effects at the alpha7 nicotinic acetylcholine receptors and N-methyl-D-aspartate receptors, may counteract the effects of KYNA. The aim of this review is to highlight the effectiveness of IDO-1, KAT II, and KMO inhibitors, as well as the galantamine-memantine combination in the modulation of different KP metabolites. KAT II inhibitors are capable of decreasing the KYNA levels in the rat brain by a maximum of 80%. KMO inhibitors effectively reduce the central nervous system (CNS) levels of 3-HK, while markedly boosting the brain concentration of KYNA. Emerging data suggest that the galantamine-memantine combination also lowers L-TRP, kynurenine, KYNA, and PIC levels in humans. Presently, there are only two pathophysiological mechanisms (cholinergic and glutamatergic) that are FDA approved for the treatment of cognitive dysfunction for which purpose the galantamine-memantine combination has been designed for clinical use against Alzheimer’s disease. The alpha7 nicotinic-NMDA hypothesis targeted by the galantamine-memantine combination has been implicated in the pathophysiology of various CNS diseases. Similarly, KYNA is well capable of modulating the neuropathophysiology of these disorders. This is known as the KYNA-centric hypothesis, which may be implicated in the management of certain neuropsychiatric conditions. In line with this hypothesis, KYNA may be considered as the “conductor of the orchestra” for the major pathophysiological mechanisms underlying CNS disorders. Therefore, there is great opportunity to further explore and compare the biological effects of these therapeutic modalities in animal models with a special focus on their effects on KP metabolites in the CNS, and with the ultimate goal of progressing to clinical trials for many neuropsychiatric diseases.