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Previous studies have suggested that alterations in excitatory/inhibitory neurotransmitters might play a crucial role in autism spectrum disorder (ASD). Proton magnetic resonance spectroscopy (1H-MRS) can provide valuable information about abnormal brain metabolism and neurotransmitter concentrations. However, few 1H-MRS studies have been published on the imbalance of the two most abundant neurotransmitters in ASD: glutamate (Glu) and gamma-aminobutyric acid (GABA). Moreover, to our knowledge none of these published studies is performed with a study population consisting purely of high-functioning autism (HFA) adolescents. Selecting only individuals with HFA eliminates factors possibly related to intellectual impairment instead of ASD. This study aims to assess Glu and GABA neurotransmitter concentrations in HFA. Occipital concentrations of Glu and GABA plus macromolecules (GABA+) were obtained using 1H-MRS relative to creatine (Cr) in adolescents with HFA (n=15 and n=13 respectively) and a healthy control group (n=17). Multiple linear regression revealed significantly higher Glu/Cr and lower GABA+/Glu concentrations in the HFA group compared to the controls. These results imply that imbalanced neurotransmitter levels of excitation and inhibition are associated with HFA in adolescents.

Type 2 diabetes is associated with cognitive decrements, accelerated cognitive decline, and increased risk for dementia. Patients with the metabolic syndrome, a major risk factor for diabetes, may display comparable cognitive decrements as seen in type 2 diabetes. Currently, the impact of diabetes and prediabetes on cognition and the underlying organization of functional brain networks still remain to be elucidated. This study investigated whether functional brain networks are affected in type 2 diabetes and prediabetes. Forty-seven participants with diabetes, 47 participants with prediabetes, and 45 control participants underwent detailed cognitive testing and 3-Tesla resting state functional MRI. Graph theoretical network analysis was performed to investigate alterations in functional cerebral networks. Participants with diabetes displayed altered network measures, characterized by a higher normalized cluster coefficient and higher local efficiency, compared with control participants. The network measures of the participants with prediabetes fell between those with diabetes and control participants. Lower processing speed was associated with shorter path length and higher global efficiency. Participants with type 2 diabetes have altered functional brain networks. This alteration is already apparent in the prediabetic stage to a somewhat lower level, hinting at functional reorganization of the cerebral networks as a compensatory mechanism for cognitive decrements.The worldwide prevalence of diabetes is increasing rapidly, with the majority of patients having type 2 diabetes (1). Along with cardiovascular risk factors, type 2 diabetes is associated with cognitive decrements, accelerated cognitive decline, and an increased risk for dementia and Alzheimer disease (2,3). A broad range of cognitive domains are affected in type 2 diabetes, and one of the most commonly affected is processing speed (4,5). However, the underlying pathological mechanism is not yet clear.The progression of normal glucose metabolism to type 2 diabetes is a gradual process in which insulin resistance plays a crucial role. Before the clinical presentation of type 2 diabetes, insulin resistance often is accompanied by other metabolic and vascular abnormalities. The cluster of these cardiovascular risk factors is referred to as the metabolic syndrome and is considered a major risk factor for diabetes (6). Patients with the metabolic syndrome have a high likelihood for diabetes and may display comparable cognitive decrements as seen in patients with type 2 diabetes (5). Furthermore, the cardiovascular risk factors are associated with an increased risk of late-life dementia (7).

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AIMTo increase our insight in the neuronal mechanisms underlying cognitive side-effects of antiepileptic drug (AED) treatment.METHODSThe relation between functional magnetic resonance-acquired brain network measures, AED use, and cognitive function was investigated. Three groups of patients with epilepsy with a different risk profile for developing cognitive side effects were included: A “low risk” category (lamotrigine or levetiracetam, n = 16), an “intermediate risk” category (carbamazepine, oxcarbazepine, phenytoin, or valproate, n = 34) and a “high risk” category (topiramate, n = 5). Brain connectivity was assessed using resting state functional magnetic resonance imaging and graph theoretical network analysis. The Computerized Visual Searching Task was used to measure central information processing speed, a common cognitive side effect of AED treatment.RESULTSCentral information processing speed was lower in patients taking AEDs from the intermediate and high risk categories, compared with patients from the low risk category. The effect of risk category on global efficiency was significant (P < 0.05, ANCOVA), with a significantly higher global efficiency for patient from the low category compared with the high risk category (P < 0.05, post-hoc test). Risk category had no significant effect on the clustering coefficient (ANCOVA, P > 0.2). Also no significant associations between information processing speed and global efficiency or the clustering coefficient (linear regression analysis, P > 0.15) were observed.CONCLUSIONOnly the four patients taking topiramate show aberrant network measures, suggesting that alterations in functional brain network organization may be only subtle and measureable in patients with more severe cognitive side effects.

Background Lacosamide (LCM) is a novel antiepileptic drug (AED) with potential benefit as adjunctive treatment in patients with partial‐onset seizures. As yet, limited information on cognitive effects of LCM is available, especially in real‐life settings. Aims In this open clinical prospective study, the cognitive effects of LCM were evaluated when used as adjunctive antiepileptic therapy in patients with refractory epilepsy. Methods We included 33 patients aged between 16 and 74 years (mean: 37 years). All patients had a localization‐related epilepsy. Patients were assessed at baseline before starting LCM treatment and during follow‐up when the optimal clinical dose was achieved. Materials Subjective complaints were evaluated using the SIDAED; effects on cognition were evaluated using the computerized visual searching task (CVST). Results The CVST showed significant faster information processing reaction times at the second evaluation (P = 0.013), which was not correlated with seizure control, type of epilepsy, age, gender, drug load, number of concomitant drugs, dose or duration of LCM treatment. On the SIDAED, patients complained more about their cognitive function at the second evaluation (P = 0.005). For the SIDAED, a positive correlation at follow‐up was found between the total severity score and higher age (r = 0.375, P = 0.031), but not with epilepsy factors or treatment characteristics. Discussion/Conlusion Screening of the cognitive effects of LCM showed that LCM does not have negative effects on information processing speed. As this is the most sensitive function for cognitive side effects of AEDs, LCM does not seem to induce the common negative cognitive effects. Remarkably, patients complained more, especially about their cognitive function, which is possible the ‘doing better, feeling worse phenomenon’.

During systems consolidation, memories are spontaneously replayed favoring information transfer from hippocampus to neocortex. However, at present no empirically supported mechanism to accomplish a transfer of memory from hippocampal to extra-hippocampal sites has been offered. We used cultured neuronal networks on multielectrode arrays and small-scale computational models to study the effect of memory replay on the formation of memory traces. We show that input-deprived networks develop an activity⇔connectivity balance where dominant activity patterns support current connectivity. Electrical stimulation at one electrode disturbs this balance and induces connectivity changes. Intrinsic forces in recurrent networks lead to a new equilibrium with activity patterns that include the stimulus response. The new connectivity is no longer disrupted by this stimulus, indicating that networks memorize it. A different stimulus again induces connectivity changes upon first application but not subsequently, demonstrating the formation of a second memory trace. Returning to the first stimulus does not affect connectivity, indicating parallel storage of both traces. A computer model robustly reproduced experimental results, suggesting that spike-timing-dependent plasticity and short time depression suffice to store parallel memory traces, even in networks without particular circuitry constraints.

The brain can be considered a network, existing of multiple interconnected areas with various functions. MRI provides opportunities to map the large-scale network organization of the brain. We tap into the neurobiochemical dimension of these networks, as neuronal functioning and signal trafficking across distributed brain regions relies on the release and presence of neurotransmitters. Using high-field MR spectroscopic imaging at 7.0 T, we obtained a non-invasive snapshot of the spatial distribution of the neurotransmitters GABA and glutamate, and investigated interregional associations of these neurotransmitters. We demonstrate that interregional correlations of glutamate and GABA concentrations can be conceptualized as networks. Furthermore, patients with epilepsy display an increased number of glutamate and GABA connections and increased average strength of the GABA network. The increased glutamate and GABA connectivity in epilepsy might indicate a disrupted neurotransmitter balance. In addition to epilepsy, the ‘neurotransmitter networks’ concept might also provide new insights for other neurological diseases.