Delineating the cognitive-neural substrates of writing: a large scale behavioral and voxel based morphometry study

Differential cortical activation patterns: pioneering sub-classification of tinnitus with and without hyperacusis by combining audiometry, gamma oscillations, and hemodynamics

The ongoing controversies about the neural basis of tinnitus, whether linked with central neural gain or not, may hamper efforts to develop therapies. We asked to what extent measurable audiometric characteristics of tinnitus without (T) or with co-occurrence of hyperacusis (TH) are distinguishable on the level of cortical responses. To accomplish this, electroencephalography (EEG) and concurrent functional near-infrared spectroscopy (fNIRS) were measured while patients performed an attentionally demanding auditory discrimination task using stimuli within the individual tinnitus frequency (fTin) and a reference frequency (fRef). Resting-state-fMRI-based functional connectivity (rs-fMRI-bfc) in ascending auditory nuclei (AAN), the primary auditory cortex (AC-I), and four other regions relevant for directing attention or regulating distress in temporal, parietal, and prefrontal cortex was compiled and compared to EEG and concurrent fNIRS activity in the same brain areas. We observed no group differences in pure-tone audiometry (PTA) between 10 and 16 kHz. However, the PTA threshold around the tinnitus pitch was positively correlated with the self-rated tinnitus loudness and also correlated with distress in T-groups, while TH experienced their tinnitus loudness at minimal loudness levels already with maximal suffering scores. The T-group exhibited prolonged auditory brain stem (ABR) wave I latency and reduced ABR wave V amplitudes (indicating reduced neural synchrony in the brainstem), which were associated with lower rs-fMRI-bfc between AAN and the AC-I, as observed in previous studies. In T-subjects, these features were linked with elevated spontaneous and reduced evoked gamma oscillations and with reduced deoxygenated hemoglobin (deoxy-Hb) concentrations in response to stimulation with lower frequencies in temporal cortex (Brodmann area (BA) 41, 42, 22), implying less synchronous auditory responses during active auditory discrimination of reference frequencies. In contrast, in the TH-group gamma oscillations and hemodynamic responses in temporoparietal regions were reversed during active discrimination of tinnitus frequencies. Our findings suggest that T and TH differ in auditory discrimination and memory-dependent directed attention during active discrimination at either tinnitus or reference frequencies, offering a test paradigm that may allow for more precise sub-classification of tinnitus and future improved treatment approaches.

Word Reading and Spelling Processing and Acquired Dyslexia post Unilateral Stroke

This study investigates the performance of adults with cerebrovascular lesion in the right hemisphere (RHL) or left hemisphere (LHL) in word reading (TLPP) and spelling (TEPP) tasks based on the dual-route models. A total of 85 adults were assessed, divided into three groups: 10 with RHL, 15 with LHL, and 60 neurologically healthy ones. The performance of the three groups was compared in terms of the characteristics of the words (regularity, frequency, and length) and pseudowords (length), error types, and psycholinguistic effects. A cluster analysis was performed to investigate the profiles of the reading. The LHL group showed lower scores in reading and spelling tasks of words and pseudowords, as well as a higher frequency of errors. Four LHL cases were found to have an acquired dyslexia profile. This study highlights that the tasks developed in Brazil are in accordance with theoretical models of written language, and the results point to the heterogeneous performance of the cases with acquired dyslexia.

The Cognitive Makeup of Writing: Multivariate Analysis of Writing Impairments Following Stroke

Writing is a recently acquired skill to human behavioral repertoire, essential in industrialized societies. In the clinic, writing impairment is evident in one-third of stroke patients. This study aimed to find out the cognitive features that contribute to writing impairment of stroke patients in two different writing systems (logographic and phonological). Cognitive profiles were assessed using the Birmingham Cognitive Screen in two cohorts, China (244 patients) and UK (501 patients). The datasets were analyzed separately using an identical procedure. Elastic net was used to rank the importance of different cognitive abilities (features) to writing skill; and linear support vector machine was used to identify the discriminative features needed to accurately identify the stroke patients with and without writing impairments. The prediction performance was evaluated with the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). For the China cohort, writing numbers, complex figure copy, and number calculation obtained good prediction performance on writing impairments with AUC 0.85 ± 0.06, ACC (89 ± 3) %, SEN (81 ± 10) %, and SPE (90 ± 27) %. Concerning the UK data, writing numbers, number calculation, non-word reading, and auditory sustain attention achieved AUC 0.79 ± 0.04, ACC (83 ± 3) %, SEN (74 ± 9) %, and SPE (84 ± 3) %. A small number of patients in both cohorts (China: 9/69, UK: 24/137), who were impaired in writing, were consistently misclassified. Two patients, one in each cohort, showed selective impairments in writing, while all remaining patients were impaired in attention, language, and/or praxis tasks. The results showed that the capability to write numbers and manipulate them were critical features for predicting writing abilities across writing systems. Reading abilities were not a good predictor of writing impairments across both cohorts. Constructive praxis (measured by complex figure copy) was relevant to impairment classification in characters-based writing (China), while phonological abilities (measured by non-word reading) were important features for impairment prediction in alphabetic writing (UK). A small proportion minority of cases with writing deficits were related to different impairment profiles. The findings in this study highlight the multifaceted nature of writing deficits and the potential use of computation methods for revealing hidden cognitive structures in neuropsychological research.

Handwriting Declines With Human Aging: A Machine Learning Study

Background

Handwriting is an acquired complex cognitive and motor skill resulting from the activation of a widespread brain network. Handwriting therefore may provide biologically relevant information on health status. Also, handwriting can be collected easily in an ecological scenario, through safe, cheap, and largely available tools. Hence, objective handwriting analysis through artificial intelligence would represent an innovative strategy for telemedicine purposes in healthy subjects and people affected by neurological disorders.

Materials and Methods

One-hundred and fifty-six healthy subjects (61 males; 49.6 ± 20.4 years) were enrolled and divided according to age into three subgroups: Younger adults (YA), middle-aged adults (MA), and older adults (OA). Participants performed an ecological handwriting task that was digitalized through smartphones. Data underwent the DBNet algorithm for measuring and comparing the average stroke sizes in the three groups. A convolutional neural network (CNN) was also used to classify handwriting samples. Lastly, receiver operating characteristic (ROC) curves and sensitivity, specificity, positive, negative predictive values (PPV, NPV), accuracy and area under the curve (AUC) were calculated to report the performance of the algorithm.

Results

Stroke sizes were significantly smaller in OA than in MA and YA. The CNN classifier objectively discriminated YA vs. OA (sensitivity = 82%, specificity = 80%, PPV = 78%, NPV = 79%, accuracy = 77%, and AUC = 0.84), MA vs. OA (sensitivity = 84%, specificity = 56%, PPV = 78%, NPV = 73%, accuracy = 74%, and AUC = 0.7), and YA vs. MA (sensitivity = 75%, specificity = 82%, PPV = 79%, NPV = 83%, accuracy = 79%, and AUC = 0.83).

Discussion

Handwriting progressively declines with human aging. The effect of physiological aging on handwriting abilities can be detected remotely and objectively by using machine learning algorithms.