Graph analysis of verbal fluency test discriminate between patients with Alzheimer’s disease, Mild Cognitive Impairment and normal elderly controls

Verbal fluency is the ability to produce a satisfying sequence of spoken words during a given time interval. The core of verbal fluency lies in the capacity to manage the executive aspects of language. The standard scores of the semantic verbal fluency test are broadly used in the neuropsychological assessment of the elderly, and different analytical methods are likely to extract even more information from the data generated in this test. Graph theory, a mathematical approach to analyze relations between items, represents a promising tool to understand a variety of neuropsychological states. This study reports a graph analysis of data generated by the semantic verbal fluency test by cognitively healthy elderly (NC), patients with Mild Cognitive Impairment – subtypes amnestic(aMCI) and amnestic multiple domain (a+mdMCI) - and patients with Alzheimer’s disease (AD). Sequences of words were represented as a speech graph in which every word corresponded to a node and temporal links between words were represented by directed edges. To characterize the structure of the data we calculated 13 speech graph attributes (SGAs). The individuals were compared when divided in three (NC – MCI – AD) and four (NC – aMCI – a+mdMCI – AD) groups. When the three groups were compared, significant differences were found in the standard measure of correct words produced, and three SGA: diameter, average shortest path, and network density. SGA sorted the elderly groups with good specificity and sensitivity. When the four groups were compared, the groups differed significantly in network density, except between the two MCI subtypes and NC and aMCI. The diameter of the network and the average shortest path were significantly different between the NC and AD, and between aMCI and AD. SGA sorted the elderly in their groups with good specificity and sensitivity, performing better than the standard score of the task. These findings provide support for a new methodological frame to assess the strength of semantic memory through the verbal fluency task, with potential to amplify the predictive power of this test. Graph analysis is likely to become clinically relevant in neurology and psychiatry, and may be particularly useful for the differential diagnosis of the elderly.

Graph analysis of verbal fluency test discriminate between patients with Alzheimer’s disease, Mild Cognitive Impairment and normal elderly controls

Verbal fluency is the ability to produce a satisfying sequence of spoken words during a given time interval. The core of verbal fluency lies in the capacity to manage the executive aspects of language. The standard scores of the semantic verbal fluency test are broadly used in the neuropsychological assessment of the elderly, and different analytical methods are likely to extract even more information from the data generated in this test. Graph theory, a mathematical approach to analyze relations between items, represents a promising tool to understand a variety of neuropsychological states. This study reports a graph analysis of data generated by the semantic verbal fluency test by cognitively healthy elderly (NC), patients with Mild Cognitive Impairment – subtypes amnestic(aMCI) and amnestic multiple domain (a+mdMCI) - and patients with Alzheimer’s disease (AD). Sequences of words were represented as a speech graph in which every word corresponded to a node and temporal links between words were represented by directed edges. To characterize the structure of the data we calculated 13 speech graph attributes (SGAs). The individuals were compared when divided in three (NC – MCI – AD) and four (NC – aMCI – a+mdMCI – AD) groups. When the three groups were compared, significant differences were found in the standard measure of correct words produced, and three SGA: diameter, average shortest path, and network density. SGA sorted the elderly groups with good specificity and sensitivity. When the four groups were compared, the groups differed significantly in network density, except between the two MCI subtypes and NC and aMCI. The diameter of the network and the average shortest path were significantly different between the NC and AD, and between aMCI and AD. SGA sorted the elderly in their groups with good specificity and sensitivity, performing better than the standard score of the task. These findings provide support for a new methodological frame to assess the strength of semantic memory through the verbal fluency task, with potential to amplify the predictive power of this test. Graph analysis is likely to become clinically relevant in neurology and psychiatry, and may be particularly useful for the differential diagnosis of the elderly.

Alterações da default mode network provocadas pela ingestão de Ayahuasca investigadas por Ressonância Magnética Funcional

Ayahuasca is psychotropic beverage that has been used for ages by indigenous populations in South America, notably in the Amazon region, for religious and medicinal purposes. The tea is obtained by the decoction of leaves from the Psychotria viridis with the bark and stalk of a shrub, the Banisteriopsis caapi. The first is rich in N-N-dimethyltryptamine (DMT), which has an important and well-known hallucinogenic effect due to its agonistic action in serotonin receptors, specifically 5-HT2A. On the other hand, β-carbolines present in B. caapi, particularly harmine and harmaline, are potent monoamine oxidase inhibitors (MAOi). In addition, the tetrahydroharmine (THH), also present in B. caapi, acts as mild selective serotonin reuptake inhibitor and a weak MAOi. This unique composition induces a number of affective, sensitive, perceptual and cognitive changes in individuals under the effect of Ayahuasca. On the other hand, there is growing interest in the Default Mode Network (DMN), which has been consistently observed in functional neuroimaging studies. The key components of this network include structures in the brain midline, as the anterior medial frontal cortex, ventral medial frontal cortex, posterior cingulate cortex, precuneus, and some regions within the inferior parietal lobe and middle temporal gyrus. It has been argued that DMN participate in tasks involving self-judgments, autobiographical memory retrieval, mental simulations, thinking in perspective, meditative states, and others. In general, these tasks require an internal focus of attention, hence the conclusion that the DMN is associated with introspective mental activity. Therefore, this study aimed to evaluate by functional magnetic resonance imaging (fMRI) changes in DMN caused via the ingestion of Ayahuasca by 10 healthy subjects while submitted to two fMRI protocols: a verbal fluency task and a resting state acquisition. In general, it was observed that Ayahuasca causes a reduction in the fMRI signal in central nodes of DMN, such as the anterior cingulate cortex, the medial prefrontal cortex, the posterior cingulate cortex, precuneus and inferior parietal lobe. Furthermore, changes in connectivity patterns of the DMN were observed, especially a decrease in the functional connectivity of the precuneus. Together, these findings indicate an association between the altered state of consciousness experienced by individuals under the effect of Ayahuasca, and changes in the stream of spontaneous thoughts leading to an increased introspective mental activity