How to Cure Alzheimer's Disease.

There has been a lot of buzz surrounding new drug discoveries that claim to cure Alzheimer's disease (AD). However, it is crucial to keep in mind that the changes in the brain linked to AD start occurring 20-30 years before the first symptoms arise. By the time symptoms become apparent, many areas of the brain have already been affected. That's why experts are focusing on identifying the onset of the neurodegeneration processes to prevent or cure AD effectively. Scientists use biomarkers and machine learning methods to analyze AD progressions and estimate them "backward" in time to discover the beginning of the disease.

Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases.

Neurodegenerative diseases (NDs) such as Alzheimer's Disease (AD) and Parkinson's Disease (PD) are devastating conditions that can develop without noticeable symptoms, causing irreversible damage to neurons before any signs become clinically evident. NDs are a major cause of disability and mortality worldwide. Currently, there are no cures or treatments to halt their progression. Therefore, the development of early detection methods is urgently needed to delay neuronal loss as soon as possible. Despite advancements in Medtech, the early diagnosis of NDs remains a challenge at the intersection of medical, IT, and regulatory fields. Thus, this review explores "digital biomarkers" (tools designed for remote neurocognitive data collection and AI analysis) as a potential solution. The review summarizes that recent studies combining AI with digital biomarkers suggest the possibility of identifying pre-symptomatic indicators of NDs. For instance, research utilizing convolutional neural networks for eye tracking has achieved significant diagnostic accuracies. ROC-AUC scores reached up to 0.88, indicating high model performance in differentiating between PD patients and healthy controls. Similarly, advancements in facial expression analysis through tools have demonstrated significant potential in detecting emotional changes in ND patients, with some models reaching an accuracy of 0.89 and a precision of 0.85. This review follows a structured approach to article selection, starting with a comprehensive database search and culminating in a rigorous quality assessment and meaning for NDs of the different methods. The process is visualized in 10 tables with 54 parameters describing different approaches and their consequences for understanding various mechanisms in ND changes. However, these methods also face challenges related to data accuracy and privacy concerns. To address these issues, this review proposes strategies that emphasize the need for rigorous validation and rapid integration into clinical practice. Such integration could transform ND diagnostics, making early detection tools more cost-effective and globally accessible. In conclusion, this review underscores the urgent need to incorporate validated digital health tools into mainstream medical practice. This integration could indicate a new era in the early diagnosis of neurodegenerative diseases, potentially altering the trajectory of these conditions for millions worldwide. Thus, by highlighting specific and statistically significant findings, this review demonstrates the current progress in this field and the potential impact of these advancements on the global management of NDs.

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms.

Humans are a vision-dominated species; what we perceive depends on where we look. Therefore, eye movements (EMs) are essential to our interactions with the environment, and experimental findings show EMs are affected in neurodegenerative disorders (ND). This could be a reason for some cognitive and movement disorders in ND. Therefore, we aim to establish whether changes in EM-evoked responses can tell us about the progression of ND, such as Alzheimer's (AD) and Parkinson's diseases (PD), in different stages. In the present review, we have analyzed the results of psychological, neurological, and EM (saccades, antisaccades, pursuit) tests to predict disease progression with machine learning (ML) methods. Thanks to ML algorithms, from the high-dimensional parameter space, we were able to find significant EM changes related to ND symptoms that gave us insights into ND mechanisms. The predictive algorithms described use various approaches, including granular computing, Naive Bayes, Decision Trees/Tables, logistic regression, C-/Linear SVC, KNC, and Random Forest. We demonstrated that EM is a robust biomarker for assessing symptom progression in PD and AD. There are navigation problems in 3D space in both diseases. Consequently, we investigated EM experiments in the virtual space and how they may help find neurodegeneration-related brain changes, e.g., related to place or/and orientation problems. In conclusion, EM parameters with clinical symptoms are powerful precision instruments that, in addition to their potential for predictions of ND progression with the help of ML, could be used to indicate the different preclinical stages of both diseases.

Influence of Bilateral Subthalamic Nucleus Deep Brain Stimulation on the Lipid Profile in Patients With Parkinson's Disease.

Background: Subthalamic nucleus deep brain stimulation (STN-DBS) is a valuable alternative to pharmacotherapy alone in an advanced Parkinson's disease (PD). Given the growing number of patients with STN-DBS, its impact on the comorbidities should be considered. Aim: The aim of this study was to evaluate the influence of bilateral STN-DBS on the lipid profile in patients with PD. Methods: Three groups of parkinsonian patients were included: 20 treated pharmacologically-PHT group, 20 newly qualified for STN-DBS-DBS group, and 14 postoperative patients (median 30 months after surgery)-POP group. Plasma concentrations of the total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), and body weight were measured thrice in 9 ± 2 month intervals. Results: A significant increase in the LDL-C concentration is observed early after surgery in the DBS group (11.4 mg/dl, P < 0.01) followed by adverse changes in the HDL-C (-7.7 mg/dl, P = 0.01) and TG (14.1 mg/dl, P = 0.05) plasma levels. In the POP group, the average level of TC at the first visit was significantly higher (P < 0.01) than in the other groups and the TG level was higher than in the PHT group during the follow-up (P < 0.01). A strong positive correlation with body weight alteration after surgery was observed only for long-term changes in the TG levels. Conclusions: Our data indicate that STN-DBS may negatively affect the cardiometabolic profile of patients. Similarly to body weight gain, an increase in the LDL-C concentration occurred early after surgery while adverse changes in the HDL-C and TG plasma levels were more gradual.

The potential neuromodulatory impact of subthalamic nucleus deep brain stimulation on Parkinson's disease progression.

INTRODUCTION: STN-DBS has been claimed to change progressionsymptomsin animal models of PD, but information is lacking about the possible neuromodulatory role of STN-DBS in humans. The aim of this prospective controlled study was to evaluate the long-term impact of STN-DBS on motor disabilities and cognitive impairment in PD patients in comparison to Best-Medical-Therapy (BMT) and Long-term-Post-Operative (POP) groups. MATERIAL AND METHODS: Patients were divided into 3 groups: the BMT-group consisted of 20 patients treated only with pharmacotherapy, the DBS-group consisted of 20 PD patients who underwent bilateral STN-DBS (examined pre- and postoperatively) and the POP-group consisted of 14 long-term postoperative patients in median 30 month-time after DBS. UPDRS III scale was measured during 3 visits in 9 ± 2 months periods (V1, V2, V3) in total-OFF phase. Cognitive assessment was performed during each visit in total-ON phase. RESULTS: The comparable UPDRS III OFF gain was observed in both BMT-group and POP-group evaluations (p < 0.05). UPDRS III OFF results in DBS-group revealed significant UPDRS III OFF increase in ΔV2-V1 assessment (p < 0.05) with no significant UPDRS III OFF alteration in ΔV3-V2 DBS-group evaluation (p > 0.05). Cognitive assessment revealed significant alterations between DBS-group and BMT-group in working memory, executive functions and learning abilities (p < 0.05). CONCLUSIONS: The impact of STN-DBS on UPDRS III OFF score and cognitive alterations suggest its neuromodulatory role, mainly during the first 9-18 months after surgery.

Evaluating reflexive saccades and UDPRS as markers of Deep Brain Stimulation and Best Medical Treatment improvements in Parkinson's disease patients: a prospective controlled study.

INTRODUCTION: To date, there has been no clear evidence regarding the evaluation of saccades as a monitoring tool of motor impairment in Parkinson's disease (PD) Subthalamic Nucleus Deep Brain Stimulation (STN-DBS) patients. The aim of this study was to evaluate the long-term impact of STN-DBS and pharmacological treatment on reflexive saccades' (RS) parameters and UPDRS alterations. MATERIAL AND METHODS: The DBS group consisted of 20 PD patients who underwent bilateral STN-DBS. The Postoperative (POP) group consisted of 14 post-DBS patients. The Best Medical Therapy (BMT) group consisted of 20 patients on pharmacotherapy only. RS parameters and the UPDRS scale were measured during three visits in four phases of treatment (i.e. BMT-ON/OFF, DBS-ON/OFF). RESULTS: The significant UPDRS III and UPDRS. Total improvements were observed in all three study groups (p < 0.05), but RS latency improvement was stated only in the DBS group in the DBS-ON phase (p < 0.05). A significant correlation between RS latency increase and UPDRS III score worsening was found in all study groups, with the most evident effect in the UPDRS III ON phase (p < 0.05). CONCLUSION: RS parameters correlated with UPDRS III outcomes during the postoperative period in DBS-STN patients. Therefore, saccadic evaluation may be a good biomarker of the patient's response to surgical and/or pharmacological treatment.

The Neuromodulatory Impact of Subthalamic Nucleus Deep Brain Stimulation on Gait and Postural Instability in Parkinson's Disease Patients: A Prospective Case Controlled Study.

Background: Subthalamic nucleus deep brain stimulation (STN-DBS) has been an established method in improvement of motor disabilities in Parkinson's disease (PD) patients. It has been also claimed to have an impact on balance and gait disorders in PD patients, but the previous results are conflicting. Objective: The aim of this prospective controlled study was to evaluate the impact of STN-DBS on balance disorders in PD patients in comparison with Best-Medical-Therapy (BMT) and Long-term-Post-Operative (POP) group. Methods: DBS-group consisted of 20 PD patients (8F, 12M) who underwent bilateral STN DBS. POP-group consisted of 14 post-DBS patients (6F, 8M) in median 30 months-time after surgery. Control group (BMT-group) consisted of 20 patients (11F, 9M) who did not undergo surgical intervention. UPDRS III scale and balance tests (Up And Go Test, Dual Task- Timed Up And Go Test, Tandem Walk Test) and posturography parameters were measured during 3 visits in 9 ± 2months periods (V1, V2, V3) 4 phases of treatment (BMT-ON/OFF, DBS-ON/OFF). Results: We have observed the slowdown of gait and postural instability progression in first 9 post-operative months followed by co-existent enhancement of balance disorders in next 9-months evaluation (p < 0.05) in balance tests (Up and Go, TWT) and in posturography examination parameters (p < 0.05). The effect was not observed neither in BMT-group nor POP-group (p > 0.05): these groups revealed constant progression of static and dynamic instability (p > 0.05). Conclusions: STN-DBS can have modulatory effect on static and dynamic instability in PD patients: it can temporarily improve balance disorders. mainly during first 9 post-operative months, but with possible following deterioration of the symptoms in next post-operative months.

Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson's Patients.

We still do not know how the brain and its computations are affected by nerve cell deaths and their compensatory learning processes, as these develop in neurodegenerative diseases (ND). Compensatory learning processes are ND symptoms usually observed at a point when the disease has already affected large parts of the brain. We can register symptoms of ND such as motor and/or mental disorders (dementias) and even provide symptomatic relief, though the structural effects of these are in most cases not yet understood. It is very important to obtain early diagnosis, which can provide several years in which we can monitor and partly compensate for the disease's symptoms, with the help of various therapies. In the case of Parkinson's disease (PD), in addition to classical neurological tests, measurements of eye movements are diagnostic. We have performed measurements of latency, amplitude, and duration in reflexive saccades (RS) of PD patients. We have compared the results of our measurement-based diagnoses with standard neurological ones. The purpose of our work was to classify how condition attributes predict the neurologist's diagnosis. For n = 10 patients, the patient age and parameters based on RS gave a global accuracy in predictions of neurological symptoms in individual patients of about 80%. Further, by adding three attributes partly related to patient 'well-being' scores, our prediction accuracies increased to 90%. Our predictive algorithms use rough set theory, which we have compared with other classifiers such as Naïve Bayes, Decision Trees/Tables, and Random Forests (implemented in KNIME/WEKA). We have demonstrated that RS are powerful biomarkers for assessment of symptom progression in PD.

Primate area V1: largest response gain for receptive fields in the straight-ahead direction.

Although neuronal responses in behaving monkeys are typically studied while the monkey fixates straight ahead, it is known that eye position modulates responses of visual neurons. The modulation has been found to enhance neuronal responses when the receptive field is placed in the straight-ahead position for neurons receiving input from the peripheral but not the central retina. We studied the effect of eye position on the responses of V1 complex cells receiving input from the central retina (1.1-5.7° eccentricity) while minimizing the effect of fixational eye movements. Contrast response functions were obtained separately with drifting light and dark bars. Data were fit with the Naka-Rushton equation: r(c)=Rmax×c/(c+c50)+s, where r(c) is mean spike rate at contrast c, Rmax is the maximum response, c50 is the contrast that elicits half of Rmax, and s is the spontaneous activity. Contrast sensitivity as measured by c50 was not affected by eye position. For dark bars, there was a statistically significant decline in the normalized Rmax with increasing deviation from straight ahead. Data for bright bars showed a similar trend with a less rapid decline. Our results indicate that neurons representing the central retina show a bias for the straight-ahead position resulting from modulation of the response gain without an accompanying modulation of contrast sensitivity. The modulation is especially obvious for dark stimuli, which might be useful for directing attention to hazardous situations such as dark holes or shadows concealing important objects (Supplement 1: Video Abstract, Supplemental digital content 1, http://links.lww.com/WNR/A295).

Localization of the subthalamic nucleus in Parkinson disease using multiunit activity.

BACKGROUND: Refinement of the subthalamic nucleus (STN) coordinates using intraoperative microelectrode recordings (MER) is routinely performed during deep brain stimulation (DBS) surgeries in Parkinson disease (PD). The commonly used criteria for electrophysiological localization of the STN are qualitative. The goal of this study was to validate quantitative STN detection algorithm (QD) derived from the multi-unit activity in a prospective setting. METHODS: Ten PD patients underwent STN DBS surgery. The MUA was obtained by removing large spikes close to microelectrode using wavelet method and integrating the 500-2000Hz band in the power spectral density. The qualitative intraoperative mapping of the STN using MER (IOM) versus QD was compared using Bland-Altman and Pearson's correlation analysis. RESULTS: The clinical efficacy was confirmed in all subjects. The mean difference between IOM and QD of the dorsal/ventral border was 0.31±0.84/0.44±0.47mm. Using Bland-Altman statistic, only 2/36 (5.6%) differences (one for the dorsal border and one for the ventral border) were out of ±2 sd line of measurement differences. Correlation between dorsal border/ventral border positions obtained by IOM and QD was 0.79, p<0.0001/0.91, p<0.0001. CONCLUSION: Both methods are in reasonable agreement and are strongly correlated. The QD gives objective coordinates of the STN borders at high precision and may be more accurate than IOM. Prospective blinded comparative studies where the DBS leads will be placed using either QD or IOM are warranted.

Optical filtering removes non-homogenous illumination artifacts in optical imaging.

Under constant light illumination, cortical neuronal activity slightly modulates intensity of the light reflected from cortical surface. Optical imaging of the reflected light from the cortex has now become a popular method to obtain cortical functional maps. Since the modulation signal is small, this method is very sensitive to other sources of the light intensity changes. A well-known artifact in this signal is the bias in the reflected light intensity due to the curvature of the cortex. The curvature of the cortex creates inhomogeneity in reflected light intensity with characteristic concentric-ring pattern in the functional maps (known as ring-shape artifact). It is particularly visible in single-condition maps if the number of trials is small. We demonstrate a method that can remove this and similar artifacts using an optical filter to equalize the reflected light intensity. Functional images recorded with the application of our filter - inserted into the optics between the cortex and the camera - became more uniform and distortion-free. Our approach consisting of the equalization optical filter is appropriate for experiments where inhomogeneous light reflection, single-condition maps, and a small number of trials exist, e.g. imaging studies of higher cortices in behaving monkeys.

Activity of common marmosets (Callithrix jacchus) in limited spaces: hand movement characteristics.

The increasing popularity of marmoset monkeys (Callithrix jacchus) in anatomical, behavioral, and electrophysiological studies has called for a detailed analysis of their natural behavior within limited spaces. In the present study, the authors analyzed hand movements during horizontal and vertical progressions in a cylinder. The trajectory of each hand covered the entire cylinder floor during horizontal progressions and the entire cylinder wall during vertical progressions. Different marmosets have different patterns of hand movement. The average maximum angle of hand movements for all marmosets during horizontal and vertical progressions oscillates, although the average over time is constant and similar for both hands, whereas head movements during horizontal progressions become smaller with successive progressions. Another observed difference between rats and monkeys was in the size of head and hand movements at the beginning of each experimental session. During the 1st horizontal progression, all marmosets moved their heads to a greater extent than their hands. This sequential head and hand movement is referred as bistable behavior. The bistable pattern of motor behavior, which was also observed in successive progressions, may be derived from an inherent fear of predators or exploratory interest of a novel environment.

Basic difference between brain and computer: integration of asynchronous processes implemented as hardware model of the retina.

There exists a common view that the brain acts like a Turing machine: The machine reads information from an infinite tape (sensory data) and, on the basis of the machine's state and information from the tape, an action (decision) is made. The main problem with this model lies in how to synchronize a large number of tapes in an adaptive way so that the machine is able to accomplish tasks such as object classification. We propose that such mechanisms exist already in the eye. A popular view is that the retina, typically associated with high gain and adaptation for light processing, is actually performing local preprocessing by means of its center-surround receptive field. We would like to show another property of the retina: The ability to integrate many independent processes. We believe that this integration is implemented by synchronization of neuronal oscillations. In this paper, we present a model of the retina consisting of a series of coupled oscillators which can synchronize on several scales. Synchronization is an analog process which is converted into a digital spike train in the output of the retina. We have developed a hardware implementation of this model, which enables us to carry out rapid simulation of multineuron oscillatory dynamics. We show that the properties of the spike trains in our model are similar to those found in vivo in the cat retina.

Quantification of three-dimensional exploration in the cylinder test by the common marmoset (Callithrix jacchus).

The increasing trend in use of marmoset monkeys in behavioral studies has necessitated a greater understanding of their natural behavior, especially within confined spatial environments. One way to approach this issue is to examine locomotor and exploratory strategies using the cylinder test, as done recently for the rat by Gharbawie et al. [Gharbawie OA, Whishaw PA, Whishaw IQ. The topography of three-dimensional exploration: a new quantification of vertical and horizontal exploration, postural support, and exploratory bouts in the cylinder test. Behav Brain Res 2004;151:125-35]. We have used this paradigm in conjunction with Eshkol-Wachmann movement analysis in marmoset monkeys. We provide evidence that marmosets display systematic changes in both horizontal and vertical progressions during exploration. Quantitative analysis and comparison with the extant data in rats showed both similarities and differences. For example, both species display horizontal and vertical scanning movements, although the variability is considerably greater for the marmoset. The horizontal progressions consist of head scans, turning, and stepping. The amplitude of these progressions oscillates and decreases with trial duration. Vertical progressions, which consist of rears, head scans, and descent, also strongly oscillate but with constant mean amplitude during a trial. The difference between the two species is most evident in locomotor behavior. Rats use their forepaws primarily for postural support and follow forequarter movements while body weight shifts are generated by the hind limbs. Marmosets, however, can hold a vertical position without hand support and display more complex movement characteristics during exploration. The data from this study should be of use in designing experiments in which marmosets are engaged in free-roaming behavior within a confined space.

Spatial receptive field organization of macaque V4 neurons.

Subfield analysis of the receptive fields (RFs) of parafoveal V4 complex cells demonstrates directly that most RFs are tiled by overlapping second-order excitatory inputs that for any given V4 cell are predominantly selective to the same preferred values of spatial frequency and orientation. These results extend hierarchical principles of RF organization in the spatial, orientation and spatial frequency domains, first recognized in V1, to an intermediate extrastriate cortex. Spatial interaction studies across subfields demonstrate that the responses of V4 neurons to paired stimuli may either decrease or increase as a function of inter-stimulus distance across the width axis. These intra-RF suppressions and facilitations vary independently in magnitude and spatial extent from cell to cell. These results taken together with the relatively large RF sizes of V4 neurons - as compared with RF sizes of their afferent inputs - lead us to hypothesize a novel property, namely that classes of stimulus configurations that enhance areal summation while reducing suppressive interactions between excitatory inputs will evoke especially robust responses. We tested, and found support for, this hypothesis by presenting stimuli consisting of optimally tuned sine-wave gratings visible only within an annular region and found that such stimuli vigorously activate V4 neurons at firing rates far higher than those evoked by comparable stimuli to either the full-field or central core. On the basis of these results we propose a framework for a new class of neural network models for the spatial RF organizations of prototypic V4 neurons.