15 Current Frontiers in Brain Research

• Building three-dimensional maps of the brain at micrometer resolution.
• Reconstructing entire cortical columns neuron by neuron.
• Mapping individual synapses using AI-based segmentation and classification.
• Comparing connectomes from healthy brains vs. Alzheimer’s, autism and schizophrenia.

• Serial electron microscopy with ultra-high resolution.
• Deep learning for volumetric reconstruction of synaptic networks.
• 7T and 11T fMRI for extremely detailed functional mapping.

👉 Objective: understand the brain as a hyper-complex electrical network, rather than as isolated regions.

• Connectome-guided neurosurgery that avoids damaging critical circuits.
• Early disconnection biomarkers for dementia and other neurodegenerative conditions.
• In-silico simulations of real circuits to test drugs without risk to patients.

• Designing electronic neurons that closely replicate biological firing and adaptation.
• Integrating neuromorphic chips with living brain tissue in animal models.
• Creating hybrid bio-synthetic networks where silicon and biological neurons exchange signals.

• Memristors that mimic synaptic plasticity.
• Low-power neuromorphic processors (e.g. Loihi, TrueNorth and successors).
• Flexible bioelectronic interfaces that conform to neural tissue.

👉 Objective: build hybrid systems capable of replacing, repairing or augmenting damaged or limited neural circuits.

• Memory and attention prostheses for people with hippocampal damage.
• Motor recovery support in patients with spinal cord injuries.
• Future cognitive-augmentation devices in high-demand environments (surgery, aviation, special operations).

• Mapping how astrocytes and oligodendrocytes manage energy in different brain regions.
• Studying lactate and glucose microcircuits between glia and neurons.
• Linking early changes in glial metabolism with subtle cognitive decline years before symptoms appear.

• Advanced metabolic imaging (FDG-PET, MR spectroscopy).
• Optogenetics to selectively activate or inhibit glial cells.
• Real-time monitoring of energy consumption at synaptic level.

👉 Objective: understand the brain as a fine-tuned thermodynamic system where energy is as decisive as electrical signaling.

• Early Alzheimer’s detection using metabolic signatures instead of only structural changes.
• Neuroprotective nutritional and pharmacological protocols based on brain energy demands.
• Strategies to reduce chronic cognitive fatigue in high-demand mental work.

• Measuring how the brain generates hierarchical predictions about sensory and social reality.
• Quantifying prediction errors in anxiety, depression and schizophrenia.
• Modeling the brain as a system that constantly minimizes surprise and uncertainty (active inference).

• Bayesian models and generative neural networks.
• EEG/MEG to study timing of prediction and error updating.
• fMRI to identify networks implementing these models (default-mode, salience, control networks).

👉 Objective: move from a reactive picture of the brain to one in which it is fundamentally a prediction machine.

• Anxiety therapies that recalibrate catastrophic predictions about the future.
• Depression interventions that loosen rigid internal models of self and world.
• AI agents inspired by active inference for autonomous robots and complex environments.

• Using focused ultrasound to modulate deep neurons in structures such as thalamus and amygdala.
• Applying deep TMS coils (H-coils) to reach subcortical and large-scale networks.
• Personalizing stimulation parameters according to each patient’s connectome and symptoms.

• tFUS (transcranial focused ultrasound stimulation) guided by MRI or CT imaging.
• Deep TMS protocols calibrated with previous fMRI mapping.
• Neuronavigation systems that calculate targets based on functional connectivity.

👉 Objective: regulate dysbalanced neural circuits with high spatial precision and without opening the skull.

• Treatment-resistant depression when medication has failed.
• Reduction of tremor and motor symptoms in Parkinson’s disease.
• Temporary enhancement of working memory and attention in controlled environments.

• Characterizing neuronal types that appear only in humans or advanced primates.
• Studying their role in fine-grained inhibition and control of cortical microcircuits.
• Investigating how these cells are altered in schizophrenia, autism and other disorders.

• Single-cell RNA sequencing (transcriptomics).
• 3D morphological reconstruction of individual neurons.
• Computational models of microcircuits with cell-type specificity.

👉 Objective: understand what makes the human brain unique at the level of microcircuits and cell types.

• Drugs targeting very specific neuronal populations with fewer side effects.
• Ultra-fine biomarkers for early psychiatric diagnostics.
• New AI architectures inspired by selective human-style inhibition.

• Searching for signatures of quantum coherence in subcellular structures.
• Simulating how decoherence could impact ultrafast neural processes.
• Exploring spin, vibration and electromagnetic patterns as possible quantum carriers.

• Quantum spectroscopy at very low temperatures in neural tissue.
• Open quantum system models applied to biological structures.
• Ultra-sensitive magnetometry for minuscule field variations.

👉 Objective: test, rather than assume, whether some brain processes require quantum models to be fully explained.

• New theoretical frameworks for very rapid decision-making.
• Hybrid quantum-neural computing architectures.
• Long-term possibility of quantum-informed biomedical markers.

• Reconstructing sentences a person hears using only their brain activity.
• Decoding internally spoken words (inner speech) in patients with implanted electrodes.
• Rebuilding images seen or imagined by combining brain recordings with AI vision models.

• High-resolution fMRI linked to large language models.
• Electrocorticography (ECoG) in epilepsy surgery patients.
• Deep neural networks mapping brain signals to text or images.

👉 Objective: create direct communication channels between brain and machine based on the content of thought.

• Restoring communication in paralyzed or locked-in patients.
• Brain-computer interfaces for controlling devices without movement.
• Clinical tools to study the structure of inner speech in psychiatric conditions.

• Growing organoids that develop spontaneous, brain-like electrical activity.
• Connecting organoids to electronic systems and training them on simple tasks.
• Modeling fetal brain development and human-specific mutations in vitro.

• Induced pluripotent stem cells (iPSC) differentiated into neural tissue.
• Multi-electrode arrays to record organoid activity.
• CRISPR/Cas9 editing to reproduce clinical mutations.

👉 Objective: study brain development and disease without intervening directly in living human brains.

• Personalized drug testing based on a patient’s own cells.
• Experimental models for autism, epilepsy and schizophrenia.
• Exploration of future regenerative and replacement therapies.

• Exploring whether synapses support more than classical electro-chemical signaling.
• Detecting ultra-weak biophoton emission in neural tissue.
• Studying magnon-like excitations and spin dynamics in microstructures.

• Single-photon detectors for extremely weak light signals.
• Nanoscale spintronics and magnetometry.
• High-resolution simulations of electromagnetic fields in microcircuits.

👉 Objective: determine whether synapses behave as multi-physics devices (electrical, chemical, optical and magnetic).

• New neuromodulation approaches using light or magnetic fields.
• Computing devices inspired by multi-physics synapses.
• Alternative hypotheses for fast, large-scale synchronization in the brain.

• Applying partial reprogramming to rejuvenate neurons without reverting them to stem cells.
• Reversing epigenetic marks associated with brain aging.
• Testing whether rejuvenated neurons maintain stored information while regaining plasticity.

• Targeted epigenetic editors directed to specific genomic regions.
• Single-cell methylation and chromatin sequencing.
• Animal models with accelerated aging.

👉 Objective: extend the functional youth of the brain without erasing its experiential history.

• Future therapies for Alzheimer’s and related dementias.
• Recovery of learning capacity in older adults.
• Prevention of age-related cognitive decline.

• Measuring how deep sleep strengthens key synapses and prunes weaker ones.
• Relating different sleep stages to episodic, motor and emotional memory consolidation.
• Manipulating slow-wave activity to boost learning and clearance of toxic waste.

• High-density polysomnography (EEG, EOG, EMG).
• Auditory or electrical stimulation synchronized to specific sleep phases.
• Computational models of synaptic homeostasis during sleep.

👉 Objective: understand sleep as a process of maintenance, cleaning and updating of the nervous system.

• Study and training protocols optimized with sleep-dependent consolidation.
• PTSD interventions that act on REM-related emotional processing.
• Insomnia treatments that restore healthy sleep architecture instead of just sedating.

• Reading and writing neural activity using light instead of traditional electrodes.
• Combining optogenetics with ultra-thin optical fibers to target specific circuits.
• Exploring endogenous optical signals as a less invasive readout.

• Optogenetic tools that make neurons light-sensitive.
• Flexible implantable optical fibers.
• Two-photon and related advanced microscopy techniques.

👉 Objective: build high-resolution brain-machine interfaces with minimal physical damage.

• Epilepsy treatment by modulating hyper-excitable foci.
• Sensory prostheses where information enters the brain optically.
• Research tools for mapping circuits at single-cell precision.

• Studying how activity integrates from single neurons to large-scale networks.
• Measuring how slow oscillations synchronize distant neural populations.
• Modeling consciousness as an emergent property of multi-scale integration.

• Combined EEG/MEG and fMRI in the same subjects.
• Neural network models operating at multiple spatial and temporal scales.
• Tools from complex systems and network theory.

👉 Objective: understand how local signals give rise to unified global experiences.

• Diagnostics of altered states of consciousness (coma, anesthesia, disorders).
• Improved intraoperative monitoring in neurosurgery.
• Inspiration for globally integrated AI architectures.

• Mapping how the “inner voice” is generated by fronto-temporal networks.
• Distinguishing healthy inner speech from pathological rumination.
• Modulating inner speech patterns with neurofeedback and cognitive training.

• fMRI and ECoG to locate areas that encode internally spoken words.
• Algorithms that decode inner speech from brain signals.
• Cognitive-behavioral protocols combined with neuroimaging.

👉 Objective: understand and modulate the “internal narrator” that structures subjective experience.

• Anxiety and depression therapies focused on rewriting inner dialogue.
• ADHD interventions that guide inner speech toward order and focus.
• Mental training for creativity, communication and decision-making.