On my Monday livestream I work through what's landing in the research that bears on brain aging and biohacking. This week three studies caught my attention: how hearing loss changes the way your brain processes speech, a blood lipid ratio that predicts cognitive decline years out, and an AI imaging method that distinguishes Parkinsonian disorders better than trained specialists. Before the research, I ran a live hemoencephalography session, so I'll start there.
What Is HEG Neurofeedback and How Does It Train Blood Flow?
Hemoencephalography (HEG) trains cerebral blood flow rather than electrical activity. The passive infrared version, developed by Dr. Jeff Carman, uses an infrared sensor on the forehead to read heat coming off the prefrontal cortex. When you focus, concentrate, or run a strong thought, metabolic demand goes up and a surge of warm blood follows about one to two seconds later. That lag comes from neurovascular coupling, the mechanism that delivers oxygenated blood to active tissue a beat behind the neural firing that called for it.
HEG differs from EEG neurofeedback in a useful way. With EEG neurofeedback, you train brainwaves you cannot feel or directly control, so the learning happens through thresholded reward and operant shaping. HEG has a semi-voluntary component. You can exert focus and watch the heat signal climb. In my live session I drove the signal up several times with concentration, then let it settle when I relaxed.
The use I find most interesting in the research and in my own experience is migraine. I get visual distortions that warn me a migraine is about an hour out. If I run HEG before the process entrenches, I can ramp the distortions down and the migraine does not arrive. Run it a couple of days in a row and the cycle backs off. HEG also appears to help social function in people with autism spectrum profiles, and it warms the brain up so that EEG training that follows hits harder. The effects are subtle for most people, and most notice them when HEG is stacked with other brain training.
One practical strength: HEG needs no gel or paste and tolerates movement well. That makes it workable for a kid who rocks or stims and for an elder who will not sit through electrode prep. You need an amplifier that supports it (a Pocket Neurobics Whiz or a Neurobit Optima) and software like BioExplorer or BioEra.
Does Hearing Loss Speed Up Cognitive Aging?
A study just published in Frontiers in Aging Neuroscience looked at how aging and hearing loss change the variability of neural signaling during speech recognition. The finding worth sitting with: more variability in the auditory and cortical signal tracked with better recognition of speech sounds. As hearing loss progressed, the brain recruited more tissue to do the same job of pulling speech out of the environment.
Add background noise and the system breaks down. Adults with hearing loss were already using more cortical resources to parse degraded input. Push up the background noise and speech recognition deteriorated sharply. Language and speech processing fell apart specifically under noisy conditions.
We have known for years that hearing loss tracks with faster cognitive aging, and it is now recognized as one of the largest modifiable dementia risk factors (Livingston et al., 2020). The usual explanations point to reduced social connection or to hearing loss being an epiphenomenon that simply co-occurs with aging. This study points at a mechanism. When auditory signal variability dampens, the brain becomes less efficient at extracting meaning from the input it does get. That efficiency loss correlates with declining language and speech processing even when the sound is technically audible. The transducer (the ear) and the processor (the cortex) degrade together.
The actionable read: protect hearing early. My reading of the cochlear implant literature is that intervening in severe-to-profound hearing loss can improve cognitive trajectories in older adults. The central auditory system carries cognitive load. Lighten that load and you free resources for everything else. This fits the larger picture of the critical aging window, where small efficiency losses compound over decades.
Can a Blood Lipid Ratio Predict Cognitive Decline?
The marker is the atherogenic index of plasma, or AIP. It is a ratio: triglycerides relative to HDL (the small, low-density lipoprotein fraction). The standard insulin-resistance panel misses it, and total cholesterol turns out to be a poor predictor. The very low density, small-particle lipoproteins are the troublemakers. They drive oxidative stress, glycation, and insulin resistance.
Researchers followed nearly 8,000 participants, roughly half men and half women, all over 45. About 37% developed cognitive impairment over a seven-year window. A higher AIP carried a strongly elevated risk of that impairment.
The shape of the curve is the interesting part. The population mean sat around 0.33. Below that threshold, risk stayed low even with somewhat elevated triglycerides. Above roughly one-third, risk climbed in a nonlinear escalation. At the very high end the additional risk increase flattened again. The system shows a sensitivity band: a little triglyceride load produces little response, a bit more destabilizes things aggressively and drives the cognitive changes over time.
The mechanism ties back to metabolism. Oxidation of fats is a major driver of insulin resistance, and insulin resistance, inflammation, and sugar feed each other in a loop. Dietary cholesterol and saturated fat do not reliably push triglycerides and VLDL up. Dietary sugar and starch do. Keep dietary sugars and starches relatively low and you are less likely to drive AIP up to that one-third tipping point.
You can calculate this yourself. On your next lipid panel, request the expanded version with VLDL and your triglycerides and do the math. Keep the ratio below one-third. This connects to what I have written about neuronal insulin resistance and brain aging and the metabolic side of brain fog.
Can AI Diagnose Parkinson's Better Than a Neurologist?
A JAMA Neurology study ran automated machine learning on MRI imaging from 645 participants with various Parkinsonian presentations. Parkinsonian disorders share features, including rigidity, tremor, gait problems, micrographia, and reduced facial expression, but split into distinct diseases with different treatments, different progressions, and different support needs.
The big ones to distinguish are Parkinson's disease itself, progressive supranuclear palsy (PSP), and multiple system atrophy (MSA). They all involve dopaminergic neurons, which control a wide range of function, so the presentations vary a lot and look superficially alike. Walk into a neurologist or geriatrician with tremor and rigidity and you are very likely to leave with a Parkinson's label, whether or not that is precisely right.
The numbers: skilled clinicians identified a Parkinsonian condition correctly about 81.6% of the time, roughly four out of five. The AI, analyzing diffusion MRI, hit 93.9%, an improvement of over 12% against trained specialists. On the harder problem of separating subtypes, the AI did Parkinson's versus atypical Parkinson's at 96%, MSA versus PSP at 98%, Parkinson's versus MSA at 98%, and Parkinson's versus PSP at 98%, with confidence intervals around 95% and accuracy ranges running up near perfect.
The imaging runs on a standard 3T MRI scanner, equipment already sitting in many hospitals, combined with support vector machine learning. Early, accurate differentiation matters because the goal of a good aging plan is compressing morbidity, holding function and then dropping late rather than sliding slowly across 30 years. With Lewy body dementia, another Parkinsonian phenomenon, the edges of the Lewy bodies become glycated and oxidized through sugar, which drives rapid progression. That is where the AIP finding and this imaging result start to fit together: oxidized fats and excess dietary sugar accelerate the same downstream pathology.
How Does This Change a Brain Aging Plan?
The through-line across all three studies: metabolic load and processing efficiency shape how your brain ages. Hearing loss raises cognitive load and degrades the efficiency of speech processing. A high AIP signals the oxidative and metabolic stress that erodes cognition over years. Accurate early AI imaging lets you identify what you are actually dealing with when planning still helps.
Compression of morbidity is the target. There is a real phenomenon called terminal drop, a rapid loss of cognitive resources in the last couple of years of life, often arriving somewhere between 78 and 88, more abrupt in people who have accumulated head injuries, diabetes, and years of metabolic wear. The strategy is to avoid the risk factors that pull that drop forward.
Practical moves from this week's research: protect your hearing and treat loss early, get your expanded lipid panel and keep AIP below one-third by lowering dietary sugars and starches, and lean toward a lower-carb, higher-protein, moderate-fat eating pattern. If you want to see what your own brain is doing electrically and functionally, a QEEG brain map is where I start, and tools like HEG and SMR neurofeedback give you something to train once the pattern is clear.