Human Brain Function Research Articles

Functional magnetic resonance imaging of depression: a bibliometrics and meta-analysis

ObjectivesThis study aims to reveal the current knowledge map, research hotspots of functional magnetic resonance imaging (fMRI) studies on depression, as well as identify the brain regions associated with depression.MethodsCiteSpace was conducted to analyze the publication outputs, country, institution, cited journals, author and cited author, references, keyword cocurrence and burst keywords of fMRI studies in depression from 2010 to 2024. And a meta-analysis of fMRI was used to identify brain regions associated with depression using Neurosynth.ResultsA total of 4,049 publications were included, and Gong Qiyong was the most prolific authors. Neuroimage, Biological Psychiatry, and Human Brain Mapping were prominent journals. Default mode network (DMN), prefrontal cortex, amygdala, and anterior cingulate cortex were the popular keywords. The fMRI studies on depression have mainly focused on major depression, especially the DMN. Functional connectivity and regional homogeneity of brain regions were research hotspots. The meta-analysis revealed significant differences in brain regions between patients with depression and healthy controls, including the Amygdala_L, Insula_R, Frontal_Inf_Oper_R, Cingulum_Post_L, Putamen_L, Thalamus_R, Angular_L, Precuneus_R, Frontal_Sup_R, Occipital_Inf_L.ConclusionsThis study sheds light on key issues and future directions in fMRI research on depression, elucidating the brain regions related to depression.Graphical

White matter functional networks in the developing brain

BackgroundFunctional magnetic resonance imaging (fMRI) is widely used to depict neural activity and understand human brain function. Studies show that functional networks in gray matter undergo complex transformations from neonatal age to childhood, supporting rapid cognitive development. However, white matter functional networks, given the much weaker fMRI signal, have not been characterized until recently, and changes in white matter functional networks in the developing brain remain unclear.PurposeAims to examine and compare white matter functional networks in neonates and 8-year-old children.MethodsWe acquired resting-state fMRI data on 69 full-term healthy neonates and 38 healthy 8-year-old children using a same imaging protocol and studied their brain white matter functional networks using a similar pipeline. First, we utilized the ICA method to extract white matter functional networks. Next, we analyzed the characteristics of the white matter functional networks from both time-domain and frequency-domain perspectives, specifically, intra-network functional connectivity (intra-network FC), inter-network functional connectivity (inter-network FC), and fractional amplitude of low-frequency fluctuation (fALFF). Finally, the differences in the above functional networks’ characteristics between the two groups were evaluated. As a supplemental measure and to confirm with literature findings on gray matter functional network changes in the developing brain, we also studied and reported functional networks in gray matter.ResultsWhite matter functional networks in the developing brain can be depicted for both the neonates and the 8-year-old children. White matter intra-network FC within the optic radiations, corticospinal tract, and anterior corona radiata was lower in 8-year-old children compared to neonates (p < 0.05). Inter-network FC between cerebral peduncle (CP) and anterior corona radiation (ACR) was higher in 8-year-olds (p < 0.05). Additionally, 8-year-olds showed a greater distribution of brain activity energy in the high-frequency range of 0.01–0.15 Hz. Significant developmental differences in brain white matter functional networks exist between the two group, characterized by increased inter-network FC, decreased intra-network FC, and higher high-frequency energy distribution. Similar findings were also observed in gray matter functional networks.ConclusionWhite matter functional networks can be reliably measured in the developing brain, and the differences in these networks reflect functional differentiation and integration in brain development.

Tangent space functional reconfigurations in individuals at risk for alcohol use disorder

Abstract Human brain function dynamically adjusts to ever-changing stimuli from the external environment. Studies characterizing brain functional reconfiguration are nevertheless scarce. Here we present a principled mathematical framework to quantify brain functional reconfiguration when engaging and disengaging from a stop signal task (SST). We apply tangent space projection (a Riemannian geometry mapping technique) to transform functional connectomes (FCs) of 54 participants and quantify functional reconfiguration using the correlation distance of the resulting tangent-FCs. Our goal was to compare functional reconfigurations in individuals at risk for alcohol use disorder (AUD). We hypothesized that functional reconfigurations when transitioning to/from a task would be influenced by family history of alcohol use disorder (FHA) and other AUD risk factors. Multilinear regression models showed that engaging and disengaging functional reconfiguration were associated with FHA and recent drinking. When engaging in the SST after a rest condition, functional reconfiguration was negatively associated with recent drinking, while functional reconfiguration when disengaging from the SST was negatively associated with FHA. In both models, several other factors contributed to the functional reconfiguration. This study demonstrates that tangent-FCs can characterize task-induced functional reconfiguration, and that it is related to AUD risk.

From lab to life: challenges and perspectives of fNIRS for haemodynamic-based neurofeedback in real-world environments.

Neurofeedback allows individuals to monitor and self-regulate their brain activity, potentially improving human brain function. Beyond the traditional electrophysiological approach using primarily electroencephalography, brain haemodynamics measured with functional magnetic resonance imaging (fMRI) and more recently, functional near-infrared spectroscopy (fNIRS) have been used (haemodynamic-based neurofeedback), particularly to improve the spatial specificity of neurofeedback. Over recent years, especially fNIRS has attracted great attention because it offers several advantages over fMRI such as increased user accessibility, cost-effectiveness and mobility-the latter being the most distinct feature of fNIRS. The next logical step would be to transfer haemodynamic-based neurofeedback protocols that have already been proven and validated by fMRI to mobile fNIRS. However, this undertaking is not always easy, especially since fNIRS novices may miss important fNIRS-specific methodological challenges. This review is aimed at researchers from different fields who seek to exploit the unique capabilities of fNIRS for neurofeedback. It carefully addresses fNIRS-specific challenges and offers suggestions for possible solutions. If the challenges raised are addressed and further developed, fNIRS could emerge as a useful neurofeedback technique with its own unique application potential-the targeted training of brain activity in real-world environments, thereby significantly expanding the scope and scalability of haemodynamic-based neurofeedback applications.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Integrating brainstem and cortical functional architectures.

The brainstem is a fundamental component of the central nervous system, yet it is typically excluded from in vivo human brain mapping efforts, precluding a complete understanding of how the brainstem influences cortical function. In this study, we used high-resolution 7-Tesla functional magnetic resonance imaging to derive a functional connectome encompassing cortex and 58 brainstem nuclei spanning the midbrain, pons and medulla. We identified a compact set of integrative hubs in the brainstem with widespread connectivity with cerebral cortex. Patterns of connectivity between brainstem and cerebral cortex manifest as neurophysiological oscillatory rhythms, patterns of cognitive functional specialization and the unimodal-transmodal functional hierarchy. This persistent alignment between cortical functional topographies and brainstem nuclei is shaped by the spatial arrangement of multiple neurotransmitter receptors and transporters. We replicated all findings using 3-Tesla data from the same participants. Collectively, this work demonstrates that multiple organizational features of cortical activity can be traced back to the brainstem.

Molecular Imaging of Human Brain Organoids Using Mass Spectrometry.

Brain organoid models serve as a powerful tool for studying human brain development and function. Mass spectrometry imaging (MSI), a cutting-edge technology, allows us to map the spatial distribution of diverse molecules such as lipids, sugars, amino acids, drugs, and their metabolites within these organoids, all without the need for specific molecular probes. High-quality MSI data hinge on meticulous sample preparation. Fixatives play a pivotal role, but conventional options such as glutaraldehyde, paraformaldehyde, and cryopreserving such as sucrose may inadvertently impact tissue metabolites. Optimal fixation entails flash freezing in liquid nitrogen. However, for small organoids, a more suitable approach involves transitioning the organoids directly from the incubator into a warmed embedding solution, followed by freezing in dry ice-cooled ethanol. Another critical step is the embedding prior to cryosectioning, which also requires materials compatible with MSI, as traditional options can interfere with matrix deposition and ionization. Here, an optimized protocol for high resolution-MALDI-MSI of human brain organoids is presented, encompassing sample preparation, sectioning, and imaging using mass spectrometry. This method showcases the molecular distribution of small metabolites, such as amino acids, with high mass accuracy and sensitivity. As such, coupled with complementary studies of brain organoids, it can assist in illuminating complex processes governing early brain development, metabolic cell fate trajectories, and distinctive metabolite signatures. Furthermore, it provides insights into the precise locations of molecules within the organoid, enriching our understanding of the spatial organization of 3D brain organoid models. As the field continues to advance, a growing number of studies leveraging MSI to delve into brain organoids and complex biological systems is anticipated, thereby deepening the understanding of the metabolic aspects of human brain function and development.

RS-MAE: Region-State Masked Autoencoder for Neuropsychiatric Disorder Classifications Based on Resting-State fMRI.

Dynamic functional connectivity (DFC) extracted from resting-state functional magnetic resonance imaging (fMRI) has been widely used for neuropsychiatric disorder classifications. However, serious information redundancy within DFC matrices can significantly undermine the performance of classification models based on them. Moreover, traditional deep models cannot adapt well to connectivity-like data, and insufficient training samples further hinder their effective training. In this study, we proposed a novel region-state masked autoencoder (RS-MAE) for proficient representation learning based on DFC matrices and ultimately neuropsychiatric disorder classifications based on fMRI. Three strategies were taken to address the aforementioned limitations. First, masked autoencoder (MAE) was introduced to reduce redundancy within DFC matrices and learn effective representations of human brain function simultaneously. Second, region-state (RS) patch embedding was proposed to replace space-time patch embedding in video MAE to adapt to DFC matrices, in which only topological locality, rather than spatial locality, exists. Third, random state concatenation (RSC) was introduced as a DFC matrix augmentation approach, to alleviate the problem of training sample insufficiency. Neuropsychiatric disorder classifications were attained by fine-tuning the pretrained encoder included in RS-MAE. The performance of the proposed RS-MAE was evaluated on four publicly available datasets, achieving accuracies of 76.32%, 77.25%, 88.87%, and 76.53% for the attention deficit and hyperactivity disorder (ADHD), autism spectrum disorder (ASD), Alzheimer's disease (AD), and schizophrenia (SCZ) classification tasks, respectively. These results demonstrate the efficacy of the RS-MAE as a proficient deep learning model for neuropsychiatric disorder classifications.

Brain Mappers of Tomorrow: An international multilingual initiative for neuroscience dissemination

The initiative “Brain Mappers of Tomorrow” coordinated by the Organization for Human Brain Mapping (OHBM) Diversity and Inclusivity Committee (DIC) aims to make neuroscience accessible to children worldwide, particularly those from historically underrepresented backgrounds. Over the past several years, this successful initiative has grown and evolved, offering live reviews of scientific papers tailored for children in multiple languages. These live review events have seen exponential growth, engaging over 1,000 children in 2023 alone. Through partnerships and innovative strategies, the initiative has successfully reached diverse audiences, fostering curiosity and critical thinking in young minds. Although some challenges remain, including recruiting scientists and participants from underprivileged communities, ongoing efforts strive to overcome these barriers. The success of “Brain Mappers of Tomorrow” demonstrates the potential for similar initiatives across scientific disciplines, emphasizing the importance of diversity and inclusivity in science education and outreach. Such efforts can foster positive impacts at multiple levels, from individuals (children and presenters) to global society. This editorial highlights the benefits and challenges of such initiatives, shares experiences and resources to assist other scientific communities in launching similar endeavors, and discusses future directions.

The gut microbiome and the brain.

The importance of the gut microbiome for human health and well-being is generally accepted, and elucidating the signaling pathways between the gut microbiome and the host offers novel mechanistic insight into the (patho)physiology and multifaceted aspects of healthy aging and human brain functions. The gut microbiome is tightly linked with the nervous system, and gut microbiota are increasingly emerging as important regulators of emotional and cognitive performance. They send and receive signals for the bidirectional communication between gut and brain via immunological, neuroanatomical, and humoral pathways. The composition of the gut microbiota and the spectrum of metabolites and neurotransmitters that they release changes with increasing age, nutrition, hypoxia, and other pathological conditions. Changes in gut microbiota (dysbiosis) are associated with critical illnesses such as cancer, cardiovascular, and chronic kidney disease but also neurological, mental, and pain disorders, as well as chemotherapies and antibiotics affecting brain development and function. Dysbiosis and a concomitant imbalance of mediators are increasingly emerging both as causes and consequences of diseases affecting the brain. Understanding the microbiota's role in the pathogenesis of these disorders will have major clinical implications and offer new opportunities for therapeutic interventions.

Whole-Head Noninvasive Brain Signal Measurement System with High Temporal and Spatial Resolution Using Static Magnetic Field Bias to the Brain.

Noninvasive brain signal measurement techniques are crucial for understanding human brain function and brain-machine interface applications. Conventionally, noninvasive brain signal measurement techniques, such as electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and near-infrared spectroscopy, have been developed. However, currently, there is no practical noninvasive technique to measure brain function with high temporal and spatial resolution using one instrument. We developed a novel noninvasive brain signal measurement technique with high temporal and spatial resolution by biasing a static magnetic field emitted from a coil on the head to the brain. In this study, we applied this technique to develop a groundbreaking system for noninvasive whole-head brain function measurement with high spatiotemporal resolution across the entire head. We validated this system by measuring movement-related brain signals evoked by a right index finger extension movement and demonstrated that the proposed system can measure the dynamic activity of brain regions involved in finger movement with high spatiotemporal accuracy over the whole brain.

Human-unique brain cell clusters are associated with learning disorders and human episodic memory activity.

The advanced evolution of the human cerebral cortex forms the basis for our high-level cognitive functions. Through a comparative analysis of single-nucleus transcriptome data from the human neocortex and that of chimpanzees, macaques, and marmosets, we discovered 20 subgroups of cell types unique to the human brain, which include 11 types of excitatory neurons. Many of these human-unique cell clusters exhibit significant overexpression of genes regulated by human-specific enhancers. Notably, these specific cell clusters also express genes associated with disease risk, particularly those related to brain dysfunctions like learning disorders. Furthermore, genes linked to cortical thickness and human episodic memory encoding activities show heightened expression within these cell subgroups. These findings underscore the critical role of human brain-unique cell clusters in the evolution of human brain functions.

The Interaction Effects of Sex, Age, APOE and Common Health Risk Factors on Human Brain Functions.

Nonlinear changes in brain function during aging are shaped by a complex interplay of factors, including sex, age, genetics, and modifiable health risk factors. However, the combined effects and underlying mechanisms of these factors on brain functional connectivity remain poorly understood. To comprehensively investigate the combined associations of sex, age, APOE genotypes, and ten common modifiable health risk factors with brain functional connectivities during aging. This analysis used data from 36,630 UK Biobank participants, aged 44-81, who were assessed for sex, age, APOE genotypes, 10 health risk factors, and brain functional connectivities through resting-state functional magnetic resonance imaging. Brain functional connectivities were evaluated through within- and between-network functional connectivities and connectivity strength. Associations between risk factors and brain functional connectivities, including their interaction effects, were analyzed. Hypertension, BMI, and education were the top three influential factors. Sex-specific effects were also observed in interactions involving APOE4 gene, smoking, alcohol consumption, diabetes, BMI, and education. Notably, a negative sex-excessive alcohol interaction showed a stronger negative effect on functional connectivities in males, particularly between the dorsal attention network and the language network, while moderate alcohol consumption appeared to have protective effects. A significant negative interaction between sex and APOE4 revealed a greater reduction in functional connectivity between the cingulo-opercular network and the posterior multimodal network in male APOE4 carriers. Additional findings included a negative age-BMI interaction between the visual and dorsal attention networks, and a positive age-hypertension interaction between the frontoparietal and default mode networks. The findings highlight significant sex disparities in the associations between age, the APOE-ε4 gene, modifiable health risk factors, and brain functional connectivity, emphasizing the necessity of jointly considering these factors to gain a deeper understanding of the complex processes underlying brain aging.

Current status and future challenges in brain imaging: Insights from leading experts

The field of brain imaging is rapidly evolving, driven by technological advancements and a deeper understanding of the brain’s complexity. To gain insights into the current status and future directions in brain imaging, we interviewed 15 renowned scholars during the 2023 and 2024 Annual Conferences of the Organization for Human Brain Mapping (OHBM). These experts provided valuable perspectives on the remarkable progress and significant challenges facing the field. Here, we offer brief summaries of these discussions in their own words.

Generalized models for estimating cerebral lateralisation of young children using functional transcranial Doppler ultrasound.

Thompson et al., 2023 (Generalized models for quantifying laterality using functional transcranial Doppler ultrasound. Human Brain Mapping, 44(1), 35-48) introduced generalised model-based analysis methods for determining cerebral lateralisation from functional transcranial Doppler ultrasound (fTCD) data which substantially decreased the uncertainty of individual lateralisation estimates across several large adult samples. We aimed to assess the suitability of these methods for increasing precision in lateralisation estimates for child fTCD data. We applied these methods to adult fTCD data to establish the validity of two child-friendly language and visuospatial tasks. We also applied the methods to fTCD data from 4- to 7-year-old children. For both samples, the laterality estimates from the complex generalised additive model (GAM) approach correlated strongly with the traditional methods while also decreasing individual standard errors compared to the popular period-of-interest averaging method. We recommend future research using fTCD with young children consider using GAMs to reduce the noise in their LI estimates.

Common and unique brain aging patterns between females and males quantified by large-scale deep learning.

There has been extensive evidence that aging affects human brain function. However, there is no complete picture of what brain functional changes are mostly related to normal aging and how aging affects brain function similarly and differently between males and females. Based on resting-state brain functional connectivity (FC) of 25,582 healthy participants (13,373 females) aged 49-76 years from the UK Biobank project, we employ deep learning with explainable AI to discover primary FCs related to progressive aging and reveal similarity and difference between females and males in brain aging. Using a nested cross-validation scheme, we conduct 4200 deep learning models to classify all paired age groups on the main data for females and males separately and then extract gender-common and gender-specific aging-related FCs. Next, we validate those FCs using additional 21,000 classifiers on the independent data. Our results support that aging results in reduced brain functional interactions for both females and males, primarily relating to the positive connectivity within the same functional domain and the negative connectivity between different functional domains. Regions linked to cognitive control show the most significant age-related changes in both genders. Unique aging effects in males and females mainly involve the interaction between cognitive control and the default mode, vision, auditory, and frontoparietal domains. Results also indicate females exhibit faster brain functional changes than males. Overall, our study provides new evidence about common and unique patterns of brain aging in females and males.

Closed-loop auditory stimulation targeting alpha and theta oscillations during REM sleep induces phase-dependent power and frequency changes.

Alpha and theta oscillations characterize the waking human electroencephalogram (EEG) and can be modulated by closed-loop auditory stimulation (CLAS). These oscillations also occur during rapid eye movement (REM) sleep, but their function here remains elusive. CLAS represents a promising tool to pinpoint how these brain oscillations contribute to brain function in humans. Here we investigate whether CLAS can modulate alpha and theta oscillations during REM sleep in a phase-dependent manner. We recorded high-density EEG during an extended overnight sleep period in 18 healthy young adults. Auditory stimulation was delivered during both phasic and tonic REM sleep in alternating 6s ON and 6s OFF windows. During the ON windows, stimuli were phase-locked to four orthogonal phases of ongoing alpha or theta oscillations detected in a frontal electrode. The phases of ongoing alpha and theta oscillations were targeted with high accuracy during REM sleep. Alpha and theta CLAS induced phase-dependent changes in power and frequency at the target location. Frequency-specific effects were observed for alpha trough (speeding up) and rising (slowing down) and theta trough (speeding up) conditions. CLAS-induced phase-dependent changes were observed during both REM sleep substages, even though auditory evoked potentials were very much reduced in phasic compared to tonic REM sleep. This study provides evidence that faster REM sleep rhythms can be modulated by CLAS in a phase-dependent manner. This offers a new approach to investigate how modulation of REM sleep oscillations affects the contribution of this vigilance state to brain function.

Diffusion Magnetic Resonance Imaging and Human Reward System Research: A Bibliometric Analysis and Visualisation of Current Research Trends.

The human reward system has been extensively studied using neuroimaging. This bibliometric analysis aimed to determine the global trend in diffusion magnetic resonance imaging (dMRI) and human reward research in terms of the number of documents, the most active countries and their collaborating countries, the top journals and institutions, the most prominent authors and most cited articles, and research hotspots. The research datasets were acquired from the Scopus database. The search terms used were 'reward' AND 'human' AND 'diffusion imaging' OR 'diffusion tensor imaging' OR 'diffusion MRI' OR 'diffusion-weighted imaging' OR 'tractography' in the abstract, article title and keywords. A total of 336 publications were analysed using Harzing's Publish or Perish and VOSviewer software. The results revealed an upward trend in the number of publications with the highest number of articles in 2020 and 2022. Most publications were limited to countries, authors, and institutions in the USA, China and Europe. Bracht, Coenen, Wiest, Federspiel and Feng were among the top authors from Switzerland, Germany and the UK. Neuroimage, Neuroimage Clinical, Frontiers in Human Neuroscience, Human Brain Mapping, and the Journal of Neuroscience were the top journals. Among the top articles, six were reviews and four were original articles, while the top keywords in human reward research were 'diffusion MRI', 'adolescence', 'depression' and 'reward-related brain areas'. These findings may serve as researchers' references to find collaborative authors, relevant journals, cooperative countries/institutions, and hot topics related to dMRI and reward research.

Effects of two chd2-knockout strains on the morphology and behavior in zebrafish.

The chromodomain helicase DNA binding domain 2 (CHD2) gene is an ATPase and a member of the SNF2-like family of helicase-related enzymes. CHD2 plays critical roles in human brain development and function, and homozygous mutation of Chd2 in mice results in perinatal lethality. To further elucidate the effects of chd2, we used CRISPR/Cas9 to create two chd2-knockout strains (fdu901, 11,979-11982delGGGT, and fdu902, 27350delG) in zebrafish. We found that the deformity and mortality rates of fdu901 and fdu902 were higher than those of the wild type. Developmental delay was more obvious and embryo mortality was higher in fdu901 than in fdu902. However, the embryo deformity rate in fdu902 was higher than that in fdu901. Although there were no significant differences in behavior between the two knockout zebrafish and wild-type zebrafish at 7days post fertilization (dpf), fdu901 and fdu902 zebrafish showed different alterations. The excitability of fdu902 was higher than that of fdu901. Overall, our data demonstrate that two homozygous chd2 knockout mutations were survivable and could be stably inherited and that fdu901 and fdu902 zebrafish differed in behavior and morphology. These two models might be good tools for understanding the functions of the different domains of chd2.

Assessing Kurt Goldstein's lasting influence in the neuropsychology of language versus his use of aphasic symptoms as diagnostic insights into brain injuries.

In the history of the neurological relationship between human behavior and brain function in Europe and North America, various perspectives on brain localization and holistic functioning have been addressed. One of the founding figures of modern neuropsychology, Professor Hans-Lukas Teuber (1916-1977) of the Massachusetts Institute of Technology, reminded the scholarly community of its negligence of preceding traditions in day-to-day research endeavors. Teuber particularly emphasized that during the development of the aphasiology field (1950s-1960s) even major figures, such as the German-American neurologist Kurt Goldstein (1878-1965), had been neglected in the scientific community's collective memory. This happened despite Goldstein's contributions to cortical blindness, vicarious brain functioning, and neurorehabilitation. The outcome of the debates regarding the neurology of language had to be incompletely relearned in later decades. Neuropsychological concerns regarding the relationship between cortical localizationism and functional holism have made recourse to Goldstein's work necessary for reviving historical answers for current conundrums. It is therefore opportune to review Goldstein's work in the light of the history of aphasiology. Contemporary scholarship has once more drawn research attention to the works of Goldstein along with Norman Geschwind (1926-1984) and his pupils. It has also resurrected the underlying research of Carl Wernicke (1848-1905). This review article explores deep and lasting questions regarding the positioning of Goldstein's holism among the contemporary holistic perspectives. It does so by firstly discussing Wernicke's traditional model of distributed localizationism. Secondly, it describes Goldstein's previous work in the German brain sciences. Thirdly, it examines his aphasiological contributions on both sides of the Atlantic. Fourthly, it addresses the advancement of a dynamic localizational perspective by Geschwind and his pupils. This article intends to render a historical analysis fruitful for those exploring modern-day problems in the neurology of aphasia and clinical speech neuropsychology.

Bacterial Production of CDKL5 Catalytic Domain: Insights in Aggregation, Internal Translation and Phosphorylation Patterns.

Cyclin-dependent kinase-like 5 (CDKL5) is a serine/threonine protein kinase involved in human brain development and functioning. Mutations in CDKL5, especially in its catalytic domain, cause a severe developmental condition named CDKL5 deficiency disorder. Nevertheless, molecular studies investigating the structural consequences of such mutations are still missing. The CDKL5 catalytic domain harbors different sites of post-translational modification, such as phosphorylations, but their role in catalytic activity, protein folding, and stability has not been entirely investigated. With this work, we describe the expression pattern of the CDKL5 catalytic domain in Escherichia coli demonstrating that it predominantly aggregates. However, the use of solubility tags, the lowering of the expression temperature, the manual codon optimization to overcome an internal translational start, and the incubation of the protein with K+ and MgATP allow the collection of a soluble catalytically active kinase. Interestingly, the resulting protein exhibits hypophosphorylation compared to its eukaryotic counterpart, proving that bacteria are a useful tool to achieve almost unmodified CDKL5. Posing questions about the CDKL5 autoactivation mechanism and the determinants for its stability, this research provides a valuable platform for comparative biophysical studies between bacterial and eukaryotic-expressed proteins, contributing to our understanding of neurodevelopmental disorders associated with CDKL5 dysfunction.